{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## This file replicates main results of the paper\n",
    "## \"Man Versus Machine Learning Revisited\"\n",
    "# by Yingguang (Conson) Zhang, Yandi Zhu, and Juhani T. Linnainmaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.tseries.offsets import *\n",
    "from tqdm import tqdm\n",
    "from functools import reduce\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from linearmodels import PanelOLS\n",
    "\n",
    "from functions import utils\n",
    "from functions import summary2\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['Times New Roman']\n",
    "plt.rcParams.update({'font.size':13})\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['grid.color'] = 'gray'\n",
    "plt.rcParams['grid.linestyle'] = '--'\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = pd.read_parquet('../data/Results/df_train_new.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "crsp = pd.read_parquet('../data/WRDS/crsp_m.parquet')\n",
    "crsp['prc'] = abs(crsp['prc'])\n",
    "crsp['ME'] = (crsp['prc']) * crsp['shrout']\n",
    "crsp.sort_values(by=['permno','YearMonth'], inplace=True)\n",
    "crsp['bh1m'] = crsp.groupby('permno')['retadj'].shift(-1)\n",
    "crsp['prc_l1'] = crsp.groupby('permno')['prc'].shift(1)\n",
    "crsp.duplicated(subset=['permno','YearMonth']).sum()\n",
    "crsp.rename(columns={'permno':'PERMNO'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## w/ look-ahead bias ##\n",
    "forecast_wLAB = pd.read_parquet('../data/Results/RF_with_lookahead_raw_005.parquet')\n",
    "forecast_wLAB = forecast_wLAB.merge(df_tmp[['permno','YearMonth','prc_l1']], on=['permno','YearMonth'])\n",
    "\n",
    "forecast_wLAB['BE_Q1'] = (forecast_wLAB['AF_q1'] - forecast_wLAB['RF_q1']) / forecast_wLAB['prc_l1']\n",
    "forecast_wLAB['BE_Q2'] = (forecast_wLAB['AF_q2'] - forecast_wLAB['RF_q2']) / forecast_wLAB['prc_l1']\n",
    "forecast_wLAB['BE_Q3'] = (forecast_wLAB['AF_q3'] - forecast_wLAB['RF_q3']) / forecast_wLAB['prc_l1']\n",
    "forecast_wLAB['BE_A1'] = (forecast_wLAB['AF_y1'] - forecast_wLAB['RF_y1']) / forecast_wLAB['prc_l1']\n",
    "forecast_wLAB['BE_A2'] = (forecast_wLAB['AF_y2'] - forecast_wLAB['RF_y2']) / forecast_wLAB['prc_l1']\n",
    "\n",
    "nonNA = (~forecast_wLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].isna()).sum(axis=1)\n",
    "forecast_wLAB['BE_Avg'] = np.where(nonNA>1,forecast_wLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].mean(axis=1,),np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## w/o look-ahead bias\n",
    "forecast_woLAB = pd.read_parquet('../data/Results/RF_wo_lookahead_raw_005.parquet')\n",
    "forecast_woLAB = forecast_woLAB.merge(df_tmp[['permno','YearMonth','prc_l1']], on=['permno','YearMonth'])\n",
    "\n",
    "forecast_woLAB['BE_Q1'] = (forecast_woLAB['AF_q1'] - forecast_woLAB['RF_q1']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_Q2'] = (forecast_woLAB['AF_q2'] - forecast_woLAB['RF_q2']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_Q3'] = (forecast_woLAB['AF_q3'] - forecast_woLAB['RF_q3']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_A1'] = (forecast_woLAB['AF_y1'] - forecast_woLAB['RF_y1']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_A2'] = (forecast_woLAB['AF_y2'] - forecast_woLAB['RF_y2']) / forecast_woLAB['prc_l1']\n",
    "\n",
    "nonNA = (~forecast_woLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].isna()).sum(axis=1)\n",
    "forecast_woLAB['BE_Avg'] = np.where(nonNA>1,forecast_woLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].mean(axis=1,),np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RF</th>\n",
       "      <th>AF</th>\n",
       "      <th>AE</th>\n",
       "      <th>RF-AE</th>\n",
       "      <th>t(RF-AE)</th>\n",
       "      <th>AF-AE</th>\n",
       "      <th>t(AF-AE)</th>\n",
       "      <th>(RF-AE)2</th>\n",
       "      <th>(AF-AE)2</th>\n",
       "      <th>Obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>0.288</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.289</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>0.028</td>\n",
       "      <td>6.502</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1024654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>0.319</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.323</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.831</td>\n",
       "      <td>0.051</td>\n",
       "      <td>10.174</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.096</td>\n",
       "      <td>1119537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>0.338</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.402</td>\n",
       "      <td>0.070</td>\n",
       "      <td>11.377</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1026893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y1</th>\n",
       "      <td>1.184</td>\n",
       "      <td>1.306</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.027</td>\n",
       "      <td>1.887</td>\n",
       "      <td>0.149</td>\n",
       "      <td>6.136</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.655</td>\n",
       "      <td>1265472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y2</th>\n",
       "      <td>1.349</td>\n",
       "      <td>1.727</td>\n",
       "      <td>1.359</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.368</td>\n",
       "      <td>8.261</td>\n",
       "      <td>1.643</td>\n",
       "      <td>1.856</td>\n",
       "      <td>1136233.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RF     AF     AE  RF-AE  t(RF-AE)  AF-AE  t(AF-AE)  (RF-AE)2  (AF-AE)2  \\\n",
       "Q1  0.288  0.317  0.289 -0.001    -0.373  0.028     6.502     0.071     0.078   \n",
       "Q2  0.319  0.374  0.323 -0.004    -0.831  0.051    10.174     0.087     0.096   \n",
       "Q3  0.338  0.411  0.341 -0.002    -0.402  0.070    11.377     0.111     0.125   \n",
       "Y1  1.184  1.306  1.157  0.027     1.887  0.149     6.136     0.602     0.655   \n",
       "Y2  1.349  1.727  1.359 -0.011    -0.204  0.368     8.261     1.643     1.856   \n",
       "\n",
       "          Obs  \n",
       "Q1  1024654.0  \n",
       "Q2  1119537.0  \n",
       "Q3  1026893.0  \n",
       "Y1  1265472.0  \n",
       "Y2  1136233.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Panel B of Table 3 ##\n",
    "## w/ look-ahead bias ##\n",
    "summary_table = []\n",
    "for i in [1,2,3]:\n",
    "    forecast_all = forecast_wLAB.dropna(subset=[f'RF_q{i}',f'AE_q{i}',f'AF_q{i}']).copy()\n",
    "    forecast_all['RF-AE'] = forecast_all[f'RF_q{i}'] - forecast_all[f'AE_q{i}']\n",
    "    forecast_all['AF-AE'] = forecast_all[f'AF_q{i}'] - forecast_all[f'AE_q{i}']\n",
    "    forecast_all['(RF-AE)2'] = (forecast_all[f'RF_q{i}'] - forecast_all[f'AE_q{i}'])**2\n",
    "    forecast_all['(AF-AE)2'] = (forecast_all[f'AF_q{i}'] - forecast_all[f'AE_q{i}'])**2\n",
    "\n",
    "    mean_t = forecast_all.groupby('YearMonth')[[f'RF_q{i}',f'AF_q{i}',f'AE_q{i}','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']].mean()\n",
    "    mean = mean_t.mean().round(3)\n",
    "    mean.index = ['RF','AF','AE','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']\n",
    "    mean['t(RF-AE)'] = sm.OLS(mean_t['RF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':3}).tvalues['const']\n",
    "    mean['t(AF-AE)'] = sm.OLS(mean_t['AF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':3}).tvalues['const']\n",
    "    mean['Obs'] = forecast_all.shape[0]\n",
    "    mean = mean.round(3)\n",
    "    mean.name = f'Q{i}'\n",
    "    summary_table.append(mean)\n",
    "\n",
    "for i in [1,2]:\n",
    "    forecast_all = forecast_wLAB.dropna(subset=[f'RF_y{i}',f'AE_y{i}',f'AF_y{i}']).copy()\n",
    "    forecast_all['RF-AE'] = forecast_all[f'RF_y{i}'] - forecast_all[f'AE_y{i}']\n",
    "    forecast_all['AF-AE'] = forecast_all[f'AF_y{i}'] - forecast_all[f'AE_y{i}']\n",
    "    forecast_all['(RF-AE)2'] = (forecast_all[f'RF_y{i}'] - forecast_all[f'AE_y{i}'])**2\n",
    "    forecast_all['(AF-AE)2'] = (forecast_all[f'AF_y{i}'] - forecast_all[f'AE_y{i}'])**2\n",
    "\n",
    "    mean_t = forecast_all.groupby('YearMonth')[[f'RF_y{i}',f'AF_y{i}',f'AE_y{i}','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']].mean()\n",
    "    mean = mean_t.mean().round(3)\n",
    "    mean.index = ['RF','AF','AE','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']\n",
    "    mean['t(RF-AE)'] = sm.OLS(mean_t['RF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':12}).tvalues['const']\n",
    "    mean['t(AF-AE)'] = sm.OLS(mean_t['AF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':12}).tvalues['const']\n",
    "    mean['Obs'] = forecast_all.shape[0]\n",
    "    mean = mean.round(3)\n",
    "    mean.name = f'Y{i}'\n",
    "    summary_table.append(mean)\n",
    "\n",
    "rlt = pd.concat(summary_table, axis=1).T[['RF','AF','AE','RF-AE','t(RF-AE)','AF-AE','t(AF-AE)','(RF-AE)2','(AF-AE)2','Obs']]\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RF</th>\n",
       "      <th>AF</th>\n",
       "      <th>AE</th>\n",
       "      <th>RF-AE</th>\n",
       "      <th>t(RF-AE)</th>\n",
       "      <th>AF-AE</th>\n",
       "      <th>t(AF-AE)</th>\n",
       "      <th>(RF-AE)2</th>\n",
       "      <th>(AF-AE)2</th>\n",
       "      <th>Obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <td>0.288</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.289</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>0.028</td>\n",
       "      <td>6.502</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.078</td>\n",
       "      <td>1024654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q2</th>\n",
       "      <td>0.318</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.323</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.946</td>\n",
       "      <td>0.051</td>\n",
       "      <td>10.174</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.096</td>\n",
       "      <td>1119537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q3</th>\n",
       "      <td>0.337</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.510</td>\n",
       "      <td>0.070</td>\n",
       "      <td>11.377</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1026893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y1</th>\n",
       "      <td>1.184</td>\n",
       "      <td>1.306</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.027</td>\n",
       "      <td>1.887</td>\n",
       "      <td>0.149</td>\n",
       "      <td>6.136</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.655</td>\n",
       "      <td>1265472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y2</th>\n",
       "      <td>1.356</td>\n",
       "      <td>1.727</td>\n",
       "      <td>1.359</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>0.368</td>\n",
       "      <td>8.261</td>\n",
       "      <td>1.805</td>\n",
       "      <td>1.856</td>\n",
       "      <td>1136233.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RF     AF     AE  RF-AE  t(RF-AE)  AF-AE  t(AF-AE)  (RF-AE)2  (AF-AE)2  \\\n",
       "Q1  0.288  0.317  0.289 -0.001    -0.373  0.028     6.502     0.071     0.078   \n",
       "Q2  0.318  0.374  0.323 -0.004    -0.946  0.051    10.174     0.090     0.096   \n",
       "Q3  0.337  0.411  0.341 -0.003    -0.510  0.070    11.377     0.120     0.125   \n",
       "Y1  1.184  1.306  1.157  0.027     1.887  0.149     6.136     0.602     0.655   \n",
       "Y2  1.356  1.727  1.359 -0.004    -0.072  0.368     8.261     1.805     1.856   \n",
       "\n",
       "          Obs  \n",
       "Q1  1024654.0  \n",
       "Q2  1119537.0  \n",
       "Q3  1026893.0  \n",
       "Y1  1265472.0  \n",
       "Y2  1136233.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Panel C of Table 3 ###\n",
    "## w/o look-ahead bias\n",
    "summary_table = []\n",
    "for i in [1,2,3]:\n",
    "    forecast_all = forecast_woLAB.dropna(subset=[f'RF_q{i}',f'AE_q{i}',f'AF_q{i}']).copy()\n",
    "    forecast_all['RF-AE'] = forecast_all[f'RF_q{i}'] - forecast_all[f'AE_q{i}']\n",
    "    forecast_all['AF-AE'] = forecast_all[f'AF_q{i}'] - forecast_all[f'AE_q{i}']\n",
    "    forecast_all['(RF-AE)2'] = (forecast_all[f'RF_q{i}'] - forecast_all[f'AE_q{i}'])**2\n",
    "    forecast_all['(AF-AE)2'] = (forecast_all[f'AF_q{i}'] - forecast_all[f'AE_q{i}'])**2\n",
    "\n",
    "    mean_t = forecast_all.groupby('YearMonth')[[f'RF_q{i}',f'AF_q{i}',f'AE_q{i}','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']].mean()\n",
    "    mean = mean_t.mean().round(3)\n",
    "    mean.index = ['RF','AF','AE','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']\n",
    "    mean['t(RF-AE)'] = sm.OLS(mean_t['RF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':3}).tvalues['const']\n",
    "    mean['t(AF-AE)'] = sm.OLS(mean_t['AF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':3}).tvalues['const']\n",
    "    mean['Obs'] = forecast_all.shape[0]\n",
    "    mean = mean.round(3)\n",
    "    mean.name = f'Q{i}'\n",
    "    summary_table.append(mean)\n",
    "    # break\n",
    "\n",
    "for i in [1,2]:\n",
    "    forecast_all = forecast_woLAB.dropna(subset=[f'RF_y{i}',f'AE_y{i}',f'AF_y{i}']).copy()\n",
    "    forecast_all['RF-AE'] = forecast_all[f'RF_y{i}'] - forecast_all[f'AE_y{i}']\n",
    "    forecast_all['AF-AE'] = forecast_all[f'AF_y{i}'] - forecast_all[f'AE_y{i}']\n",
    "    forecast_all['(RF-AE)2'] = (forecast_all[f'RF_y{i}'] - forecast_all[f'AE_y{i}'])**2\n",
    "    forecast_all['(AF-AE)2'] = (forecast_all[f'AF_y{i}'] - forecast_all[f'AE_y{i}'])**2\n",
    "\n",
    "    mean_t = forecast_all.groupby('YearMonth')[[f'RF_y{i}',f'AF_y{i}',f'AE_y{i}','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']].mean()\n",
    "    mean = mean_t.mean().round(3)\n",
    "    mean.index = ['RF','AF','AE','RF-AE','AF-AE','(RF-AE)2','(AF-AE)2']\n",
    "    mean['t(RF-AE)'] = sm.OLS(mean_t['RF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':12}).tvalues['const']\n",
    "    mean['t(AF-AE)'] = sm.OLS(mean_t['AF-AE'],exog=[1]*len(mean_t)).fit(cov_type = 'HAC', cov_kwds = {'maxlags':12}).tvalues['const']\n",
    "    mean['Obs'] = forecast_all.shape[0]\n",
    "    mean = mean.round(3)\n",
    "    mean.name = f'Y{i}'\n",
    "    summary_table.append(mean)\n",
    "\n",
    "rlt = pd.concat(summary_table, axis=1).T[['RF','AF','AE','RF-AE','t(RF-AE)','AF-AE','t(AF-AE)','(RF-AE)2','(AF-AE)2','Obs']]\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Panel C of Table 3: Accuracy Compare\n",
    "mse = []\n",
    "for h in ['q1','q2','q3','y1','y2']:\n",
    "    mse.append(((forecast_wLAB[f'RF_{h}'] - forecast_wLAB[f'AE_{h}'])**2).groupby(forecast_wLAB['YearMonth']).mean())\n",
    "mse_wLAB = pd.concat(mse, axis=1)\n",
    "\n",
    "mse = []\n",
    "for h in ['q1','q2','q3','y1','y2']:\n",
    "    mse.append(((forecast_woLAB[f'RF_{h}'] - forecast_woLAB[f'AE_{h}'])**2).groupby(forecast_woLAB['YearMonth']).mean())\n",
    "mse_woLAB = pd.concat(mse, axis=1)\n",
    "\n",
    "# Hughes et al. (2008)\n",
    "forecast_Hughes = pd.read_parquet('../data/Results/Hughes_eps.parquet')\n",
    "mse = []\n",
    "for h in ['q1','q2','q3','y1','y2']:\n",
    "    mse.append(((forecast_Hughes[f'LF_{h}'] - forecast_Hughes[f'AE_{h}'])**2).groupby(forecast_Hughes['YearMonth']).mean())\n",
    "mse_Hughes = pd.concat(mse, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(nan)</td>\n",
       "      <td>(-6.48)</td>\n",
       "      <td>(-6.84)</td>\n",
       "      <td>(nan)</td>\n",
       "      <td>(-10.50)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           y        1        2      3         4\n",
       "const  0.000   -0.003   -0.009  0.000    -0.161\n",
       "       (nan)  (-6.48)  (-6.84)  (nan)  (-10.50)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Compare MSE between w/ and w/o look-ahead bias\n",
    "mdl = []\n",
    "for i in range(5):\n",
    "    mdl.append(sm.OLS(endog=mse_wLAB[i]-mse_woLAB[i],\n",
    "                      exog=[1]*len(mse_woLAB)).fit(cov_type = 'HAC',\n",
    "                                                 cov_kwds = {'maxlags':12}))\n",
    "rlts = summary2.summary_col(mdl, float_format='%.3f')\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.30)</td>\n",
       "      <td>(1.10)</td>\n",
       "      <td>(0.91)</td>\n",
       "      <td>(-0.63)</td>\n",
       "      <td>(-0.05)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y       1       2        3        4\n",
       "const   0.002   0.003   0.004   -0.005   -0.003\n",
       "       (1.30)  (1.10)  (0.91)  (-0.63)  (-0.05)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Compare MSE between w/o look-ahead bias and Hughes et al. (2008)\n",
    "mdl = []\n",
    "for i in range(5):\n",
    "    mdl.append(sm.OLS(endog=mse_Hughes[i]-mse_woLAB[i],\n",
    "                      exog=[1]*len(mse_woLAB)).fit(cov_type = 'HAC',\n",
    "                                                 cov_kwds = {'maxlags':12}))\n",
    "rlts = summary2.summary_col(mdl, float_format='%.3f')\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.81)</td>\n",
       "      <td>(-0.07)</td>\n",
       "      <td>(-0.82)</td>\n",
       "      <td>(-0.71)</td>\n",
       "      <td>(-0.10)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y        1        2        3        4\n",
       "const   0.001   -0.000   -0.002   -0.006   -0.003\n",
       "       (0.81)  (-0.07)  (-0.82)  (-0.71)  (-0.10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pre 2006 and Post 2006\n",
    "diff_mse = mse_Hughes - mse_woLAB\n",
    "mdl = []\n",
    "for i in range(5):\n",
    "    ## Hughes et al. (2008) uses actual earnings data ends in 2006 and forecast data ends in 2004\n",
    "    # we split the data by 2004-12 to be consistent with Hughes et al. (2008)\n",
    "    pre_2006 = diff_mse[diff_mse.index<'2004-12-31']\n",
    "    mdl.append(sm.OLS(endog=pre_2006[i],\n",
    "                      exog=[1]*len(pre_2006)).fit(cov_type = 'HAC',\n",
    "                                                 cov_kwds = {'maxlags':12}))\n",
    "rlts = summary2.summary_col(mdl, float_format='%.3f')\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.16)</td>\n",
       "      <td>(1.16)</td>\n",
       "      <td>(1.27)</td>\n",
       "      <td>(-0.28)</td>\n",
       "      <td>(-0.02)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y       1       2        3        4\n",
       "const   0.004   0.006   0.011   -0.004   -0.003\n",
       "       (1.16)  (1.16)  (1.27)  (-0.28)  (-0.02)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl = []\n",
    "for i in range(5):\n",
    "    post_2006 = diff_mse[diff_mse.index>'2004-12-31']\n",
    "    mdl.append(sm.OLS(endog=post_2006[i],\n",
    "                      exog=[1]*len(post_2006)).fit(cov_type = 'HAC',\n",
    "                                                 cov_kwds = {'maxlags':12}))\n",
    "rlts = summary2.summary_col(mdl, float_format='%.3f')\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_wLAB = forecast_wLAB[['permno','YearMonth',\n",
    "                               'BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2',\n",
    "                               'RF_q1','RF_q2','RF_q3','RF_y1','RF_y2',\n",
    "                               'BE_Avg',\n",
    "                              ]].copy()\n",
    "forecast_wLAB.set_index(['permno','YearMonth'], inplace=True)\n",
    "forecast_wLAB.columns = [ f'{c}_wLAB' for c in forecast_wLAB.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_woLAB = forecast_woLAB[['permno','YearMonth',\n",
    "                               'BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2',\n",
    "                               'RF_q1','RF_q2','RF_q3','RF_y1','RF_y2',\n",
    "                               'BE_Avg',\n",
    "                              ]].copy()\n",
    "forecast_woLAB.set_index(['permno','YearMonth'], inplace=True)\n",
    "forecast_woLAB.columns = [ f'{c}_woLAB' for c in forecast_woLAB.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bias_all = forecast_wLAB.merge(forecast_woLAB, on=['permno','YearMonth'])\n",
    "# Test df\n",
    "df = df_tmp.merge(Bias_all, on=['permno','YearMonth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_factor = pd.read_csv('../data/Other/ff5_factors_m.CSV')\n",
    "all_factor['YearMonth'] = pd.to_datetime(all_factor['yyyymm'], format='%Y%m') + MonthEnd(0)\n",
    "# We do sort at the end of month t and use the return of month t+1\n",
    "# So we need to shift the factor return by 1 month\n",
    "all_factor['YearMonth'] = all_factor['YearMonth'] + MonthEnd(-1)\n",
    "\n",
    "factor_dict = {'Ret':['ones'],\n",
    "              }\n",
    "factor_dict2 = {'CAPM':['ones','Mkt_RF'],\n",
    "               'FF3':['ones','Mkt_RF','SMB','HML'],\n",
    "               'FF5':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA'],\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_wLAB, Delete 91707 rows due to missing values, raw data 1370066 rows --> new data 1278359 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ret</th>\n",
       "      <td>1.38</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(6.74)</td>\n",
       "      <td>(4.23)</td>\n",
       "      <td>(2.44)</td>\n",
       "      <td>(0.78)</td>\n",
       "      <td>(-0.51)</td>\n",
       "      <td>(-5.08)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1       2       3       4        5      H-L\n",
       "Ret    1.38    0.92    0.61    0.24    -0.21    -1.59\n",
       "     (6.74)  (4.23)  (2.44)  (0.78)  (-0.51)  (-5.08)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 4: Replication with look-ahead bias\n",
    "sort_var = 'BE_Avg_wLAB'\n",
    "num_level = 5\n",
    "_,vwret2 = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "result2 = utils.SingleSort_RetAna(_,vwret2,'YearMonth',factor_data=all_factor,factor_dict=factor_dict,lag=None)\n",
    "result2.to_clipboard()\n",
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_woLAB, Delete 91707 rows due to missing values, raw data 1370066 rows --> new data 1278359 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ret</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.83)</td>\n",
       "      <td>(4.10)</td>\n",
       "      <td>(3.62)</td>\n",
       "      <td>(2.78)</td>\n",
       "      <td>(1.81)</td>\n",
       "      <td>(-0.80)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1       2       3       4       5      H-L\n",
       "Ret    0.99    0.90    0.91    0.85    0.75    -0.25\n",
       "     (4.83)  (4.10)  (3.62)  (2.78)  (1.81)  (-0.80)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 4: Replication without look-ahead bias\n",
    "sort_var = 'BE_Avg_woLAB'\n",
    "num_level = 5\n",
    "_,vwret3 = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "result3 = utils.SingleSort_RetAna(_,vwret3,'YearMonth',factor_data=all_factor,factor_dict=factor_dict,lag=None)\n",
    "result3.to_clipboard()\n",
    "result3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 1 & 5 & 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_by_col(df1,df2):\n",
    "    '''\n",
    "    Merge two dataframes: (coefs) and (standard-errors) side-by-side\n",
    "    '''\n",
    "    new_columns = []\n",
    "\n",
    "    for col_name in df1.columns:\n",
    "        new_columns.append(df1[col_name])\n",
    "        new_columns.append(df2[col_name])\n",
    "\n",
    "    df_merged = pd.concat(new_columns, axis=1)\n",
    "\n",
    "    new_col_names = []\n",
    "    for i, col_name in enumerate(df1.columns):\n",
    "        new_col_names.append(col_name)\n",
    "        new_col_names.append('')\n",
    "\n",
    "    df_merged.columns = new_col_names\n",
    "\n",
    "    return df_merged\n",
    "\n",
    "def Factor_Exposure(vwret, num_level, time_id, factor_data, factor_dict, lag, regressor_order=['ones']):\n",
    "    '''\n",
    "    This function is used to get common risk factor exposure\n",
    "    vwret is output from SingleSort\n",
    "    e.g.:\n",
    "        Factor_Exposure(vwret1, num_level, 'YearMonth',\n",
    "                        all_factor, factor_dict2, 12,\n",
    "                        regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    '''\n",
    "    vwret = vwret.merge(factor_data, left_index=True, right_on=time_id)\n",
    "    vwret['ones'] = 1\n",
    "    coefs = []\n",
    "    coefs_t = []\n",
    "\n",
    "    # For each test factor model\n",
    "    for name, factor_list in factor_dict.items():\n",
    "\n",
    "        model_i = sm.OLS(endog=vwret['H-L'],\n",
    "                         exog=vwret[factor_list],\n",
    "                         missing='drop')\n",
    "        if lag:\n",
    "            model_i = model_i.fit(cov_type = 'HAC', cov_kwds = {'maxlags':lag})\n",
    "        else:\n",
    "            model_i = model_i.fit(cov_type = 'HC0')\n",
    "\n",
    "        coefs.append(summary2.summary_params(model_i)['Coef.'].rename(name))\n",
    "        coefs_t.append(summary2.summary_params(model_i)['t'].rename(name))\n",
    "        # model.append(model_i)\n",
    "\n",
    "    coefs = pd.concat(coefs,axis=1)\n",
    "    coefs_t = pd.concat(coefs_t,axis=1)\n",
    "    result = merge_by_col(coefs, coefs_t)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAPM</th>\n",
       "      <th></th>\n",
       "      <th>FF3</th>\n",
       "      <th></th>\n",
       "      <th>FF5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-2.00</td>\n",
       "      <td>-7.25</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-8.63</td>\n",
       "      <td>-1.71</td>\n",
       "      <td>-6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mkt_RF</th>\n",
       "      <td>0.61</td>\n",
       "      <td>7.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.43</td>\n",
       "      <td>5.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.66</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HML</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.62</td>\n",
       "      <td>5.35</td>\n",
       "      <td>0.90</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMW</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CAPM         FF3         FF5      \n",
       "ones   -2.00 -7.25 -2.10 -8.63 -1.71 -6.07\n",
       "Mkt_RF  0.61  7.49  0.56  7.56  0.43  5.71\n",
       "SMB      NaN   NaN  0.88  7.22  0.66  5.14\n",
       "HML      NaN   NaN  0.62  5.35  0.90  6.27\n",
       "RMW      NaN   NaN   NaN   NaN -0.75 -4.01\n",
       "CMA      NaN   NaN   NaN   NaN -0.30 -1.09"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 1 With Look-ahead Bias & Panel B of Table 5\n",
    "exposure2 = Factor_Exposure(vwret2, num_level, 'YearMonth',\n",
    "                all_factor, factor_dict2, None, regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "exposure2.to_clipboard()\n",
    "exposure2.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAPM</th>\n",
       "      <th></th>\n",
       "      <th>FF3</th>\n",
       "      <th></th>\n",
       "      <th>FF5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-0.67</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-3.26</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mkt_RF</th>\n",
       "      <td>0.61</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0.55</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.44</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.96</td>\n",
       "      <td>7.82</td>\n",
       "      <td>0.75</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HML</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.48</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMW</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CAPM         FF3         FF5      \n",
       "ones   -0.67 -2.45 -0.76 -3.26 -0.41 -1.54\n",
       "Mkt_RF  0.61  7.75  0.55  7.83  0.44  5.94\n",
       "SMB      NaN   NaN  0.96  7.82  0.75  5.69\n",
       "HML      NaN   NaN  0.61  5.48  0.84  5.90\n",
       "RMW      NaN   NaN   NaN   NaN -0.72 -3.83\n",
       "CMA      NaN   NaN   NaN   NaN -0.18 -0.67"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 1 Without Look-ahead Bias & Panel C of Table 5\n",
    "exposure3 = Factor_Exposure(vwret3, num_level, 'YearMonth',\n",
    "                all_factor, factor_dict2, None, regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "exposure3.to_clipboard()\n",
    "exposure3.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_woLAB, Delete 91707 rows due to missing values, raw data 1370066 rows --> new data 1278359 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FFC6</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(6.81)</td>\n",
       "      <td>(3.62)</td>\n",
       "      <td>(3.33)</td>\n",
       "      <td>(2.70)</td>\n",
       "      <td>(1.54)</td>\n",
       "      <td>(-0.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q4</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(5.69)</td>\n",
       "      <td>(3.31)</td>\n",
       "      <td>(2.02)</td>\n",
       "      <td>(1.53)</td>\n",
       "      <td>(0.84)</td>\n",
       "      <td>(-0.27)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q-factor</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(5.44)</td>\n",
       "      <td>(2.82)</td>\n",
       "      <td>(2.16)</td>\n",
       "      <td>(1.93)</td>\n",
       "      <td>(0.97)</td>\n",
       "      <td>(-0.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SY</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(5.79)</td>\n",
       "      <td>(3.60)</td>\n",
       "      <td>(3.20)</td>\n",
       "      <td>(2.77)</td>\n",
       "      <td>(1.37)</td>\n",
       "      <td>(0.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHS</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.47)</td>\n",
       "      <td>(3.02)</td>\n",
       "      <td>(2.49)</td>\n",
       "      <td>(2.01)</td>\n",
       "      <td>(1.76)</td>\n",
       "      <td>(0.87)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1       2       3       4       5      H-L\n",
       "FFC6        0.29    0.18    0.26    0.31    0.27    -0.02\n",
       "          (6.81)  (3.62)  (3.33)  (2.70)  (1.54)  (-0.11)\n",
       "q4          0.29    0.18    0.22    0.25    0.21    -0.08\n",
       "          (5.69)  (3.31)  (2.02)  (1.53)  (0.84)  (-0.27)\n",
       "q-factor    0.27    0.16    0.24    0.32    0.26    -0.01\n",
       "          (5.44)  (2.82)  (2.16)  (1.93)  (0.97)  (-0.02)\n",
       "SY          0.28    0.21    0.33    0.42    0.35     0.07\n",
       "          (5.79)  (3.60)  (3.20)  (2.77)  (1.37)   (0.25)\n",
       "DHS         0.25    0.17    0.28    0.38    0.56     0.31\n",
       "          (4.47)  (3.02)  (2.49)  (2.01)  (1.76)   (0.87)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative factor model: Table 6\n",
    "sort_var = 'BE_Avg_woLAB'\n",
    "num_level = 5\n",
    "factor_dict = {'FFC6':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA','MOM'],\n",
    "               'q4':['ones','R_MKT','R_ME','R_IA','R_ROE'],\n",
    "               'q-factor':['ones','R_MKT','R_ME','R_IA','R_ROE','R_EG'],\n",
    "               'SY':['ones','Mkt_RF','SMB_SY','MGMT', 'PERF'],\n",
    "               'DHS':['ones','Mkt_RF','PEAD', 'FIN'],\n",
    "              }\n",
    "_,vwret3 = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "result3 = utils.SingleSort_RetAna(_,vwret3,'YearMonth',factor_data=all_factor,factor_dict=factor_dict,lag=None)\n",
    "result3.to_clipboard()\n",
    "result3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>H-L I</th>\n",
       "      <th>H-L II</th>\n",
       "      <th>H-L III</th>\n",
       "      <th>Pre-Post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-1.59</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-5.08)</td>\n",
       "      <td>(-4.63)</td>\n",
       "      <td>(-3.14)</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         H-L I   H-L II  H-L III  Pre-Post\n",
       "const    -1.59    -1.82    -1.42     -0.40\n",
       "       (-5.08)  (-4.63)  (-3.14)      0.53"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## With look-ahead bias\n",
    "vwret = vwret2\n",
    "pre_2000 = vwret[vwret.index<='1999-12-31']\n",
    "post_2000 = vwret[vwret.index>'1999-12-31']\n",
    "\n",
    "mdl1 = sm.OLS(endog=vwret['H-L'], exog=[1]*len(vwret)).fit(cov_type = 'HC0')\n",
    "mdl2 = sm.OLS(endog=pre_2000['H-L'], exog=[1]*len(pre_2000)).fit(cov_type = 'HC0')\n",
    "mdl3 = sm.OLS(endog=post_2000['H-L'], exog=[1]*len(post_2000)).fit(cov_type = 'HC0')\n",
    "rlt = summary2.summary_col([mdl1, mdl2, mdl3], float_format='%.2f')\n",
    "rlt['Pre-Post'] = [np.round(pre_2000['H-L'].mean() - post_2000['H-L'].mean(),2),\n",
    "                   np.round(stats.ttest_ind(pre_2000['H-L'], post_2000['H-L']).pvalue,2)\n",
    "                  ]\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>H-L I</th>\n",
       "      <th>H-L II</th>\n",
       "      <th>H-L III</th>\n",
       "      <th>Pre-Post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.80)</td>\n",
       "      <td>(-1.35)</td>\n",
       "      <td>(-0.13)</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         H-L I   H-L II  H-L III  Pre-Post\n",
       "const    -0.25    -0.52    -0.06     -0.46\n",
       "       (-0.80)  (-1.35)  (-0.13)      0.47"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Without look-ahead bias\n",
    "vwret = vwret3\n",
    "pre_2000 = vwret[vwret.index<='1999-12-31']\n",
    "post_2000 = vwret[vwret.index>'1999-12-31']\n",
    "\n",
    "mdl1 = sm.OLS(endog=vwret['H-L'], exog=[1]*len(vwret)).fit(cov_type = 'HC0')\n",
    "mdl2 = sm.OLS(endog=pre_2000['H-L'], exog=[1]*len(pre_2000)).fit(cov_type = 'HC0')\n",
    "mdl3 = sm.OLS(endog=post_2000['H-L'], exog=[1]*len(post_2000)).fit(cov_type = 'HC0')\n",
    "rlt = summary2.summary_col([mdl1, mdl2, mdl3], float_format='%.2f')\n",
    "rlt['Pre-Post'] = [np.round(pre_2000['H-L'].mean() - post_2000['H-L'].mean(),2),\n",
    "                   np.round(stats.ttest_ind(pre_2000['H-L'], post_2000['H-L']).pvalue,2)\n",
    "                  ]\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "## To replicate this figure, we need earnings forecast data from BHL ##\n",
    "#######################################################################\n",
    "## BHL\n",
    "forecast_ORIG = pd.read_csv('../data/BHL/Conditional_Bias.csv')\n",
    "forecast_ORIG['DATE'] = pd.to_datetime(forecast_ORIG['DATE'],format='%Y-%m') + MonthEnd(0)\n",
    "forecast_ORIG.rename(columns={'PERMNO':'permno','DATE':'YearMonth'}, inplace=True)\n",
    "df = df.merge(forecast_ORIG, on=['permno','YearMonth'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:Avg_bias, Delete 24 rows due to missing values, raw data 1231112 rows --> new data 1231088 rows\n",
      "Var:BE_Avg_wLAB, Delete 24 rows due to missing values, raw data 1231112 rows --> new data 1231088 rows\n",
      "Var:BE_Avg_woLAB, Delete 24 rows due to missing values, raw data 1231112 rows --> new data 1231088 rows\n"
     ]
    }
   ],
   "source": [
    "num_level = 5\n",
    "_,vwret1 = utils.SingleSort(df.dropna(subset=['Avg_bias','BE_Avg_wLAB','BE_Avg_woLAB']),\n",
    "                             'PERMNO', 'YearMonth', 'Avg_bias', 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "_,vwret2 = utils.SingleSort(df.dropna(subset=['Avg_bias','BE_Avg_wLAB','BE_Avg_woLAB']),\n",
    "                             'PERMNO', 'YearMonth', 'BE_Avg_wLAB', 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "_,vwret3 = utils.SingleSort(df.dropna(subset=['Avg_bias','BE_Avg_wLAB','BE_Avg_woLAB']),\n",
    "                             'PERMNO', 'YearMonth', 'BE_Avg_woLAB', 'bh1m', num_level, 'ME', quantile_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9484238359248416"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vwret1['H-L'].corr(vwret2['H-L'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 982
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Figure 2\n",
    "(1-vwret1['H-L']/100).cumprod().plot(figsize=(10,5), label='BHL')\n",
    "(1-vwret2['H-L']/100).cumprod().plot(figsize=(10,5), label='With Look-ahead Bias', linestyle='--')\n",
    "(1-vwret3['H-L']/100).cumprod().plot(figsize=(10,5), label='Without Look-ahead Bias', linestyle=':',)\n",
    "plt.yscale(\"log\")\n",
    "plt.grid(True, which=\"both\", linestyle=\"--\", linewidth=0.5, alpha=0.3)\n",
    "plt.legend()\n",
    "plt.yticks([1,10,100],[1,10,100])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Return on $1 investment (log-scale)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../data/Results/cumret.png',dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table C.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_wLAB, Delete 91707 rows due to missing values, raw data 1370066 rows --> new data 1278359 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ret</th>\n",
       "      <td>1.82</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(7.31)</td>\n",
       "      <td>(5.38)</td>\n",
       "      <td>(3.42)</td>\n",
       "      <td>(1.79)</td>\n",
       "      <td>(0.99)</td>\n",
       "      <td>(-5.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FF5</th>\n",
       "      <td>1.14</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(17.28)</td>\n",
       "      <td>(12.03)</td>\n",
       "      <td>(2.88)</td>\n",
       "      <td>(-1.74)</td>\n",
       "      <td>(-1.06)</td>\n",
       "      <td>(-4.87)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1        2       3        4        5      H-L\n",
       "Ret     1.82     1.30    0.94     0.57     0.41    -1.41\n",
       "      (7.31)   (5.38)  (3.42)   (1.79)   (0.99)  (-5.21)\n",
       "FF5     1.14     0.56    0.18    -0.19    -0.29    -1.43\n",
       "     (17.28)  (12.03)  (2.88)  (-1.74)  (-1.06)  (-4.87)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Equal-weighted Portfolios ##\n",
    "df['ones'] = 1\n",
    "## With look-ahead bias\n",
    "sort_var = 'BE_Avg_wLAB'\n",
    "num_level = 5\n",
    "factor_dict = {'Ret':['ones'],\n",
    "               'FF5':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA'],\n",
    "              }\n",
    "\n",
    "_,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ones', quantile_filter=None)\n",
    "result = utils.SingleSort_RetAna(_,vwret,'YearMonth',factor_data=all_factor,factor_dict=factor_dict,lag=None)\n",
    "result.to_clipboard()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_woLAB, Delete 91707 rows due to missing values, raw data 1370066 rows --> new data 1278359 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ret</th>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.59)</td>\n",
       "      <td>(4.11)</td>\n",
       "      <td>(3.47)</td>\n",
       "      <td>(2.70)</td>\n",
       "      <td>(2.59)</td>\n",
       "      <td>(-0.24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FF5</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(7.38)</td>\n",
       "      <td>(5.89)</td>\n",
       "      <td>(2.83)</td>\n",
       "      <td>(0.79)</td>\n",
       "      <td>(1.36)</td>\n",
       "      <td>(-0.37)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1       2       3       4       5      H-L\n",
       "Ret    1.14    1.01    0.96    0.86    1.08    -0.07\n",
       "     (4.59)  (4.11)  (3.47)  (2.70)  (2.59)  (-0.24)\n",
       "FF5    0.49    0.26    0.18    0.09    0.38    -0.11\n",
       "     (7.38)  (5.89)  (2.83)  (0.79)  (1.36)  (-0.37)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Without look-ahead bias\n",
    "sort_var = 'BE_Avg_woLAB'\n",
    "num_level = 5\n",
    "_,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ones', quantile_filter=None)\n",
    "result = utils.SingleSort_RetAna(_,vwret,'YearMonth',factor_data=all_factor,factor_dict=factor_dict,lag=None)\n",
    "result.to_clipboard()\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table C.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.01)</td>\n",
       "      <td>(4.78)</td>\n",
       "      <td>(5.28)</td>\n",
       "      <td>(3.99)</td>\n",
       "      <td>(5.30)</td>\n",
       "      <td>(4.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bias</th>\n",
       "      <td>-0.2142</td>\n",
       "      <td>-0.5143</td>\n",
       "      <td>-0.6188</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>-0.0890</td>\n",
       "      <td>-0.1758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-1.78)</td>\n",
       "      <td>(-5.17)</td>\n",
       "      <td>(-6.81)</td>\n",
       "      <td>(0.10)</td>\n",
       "      <td>(-4.60)</td>\n",
       "      <td>(-3.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1024253</td>\n",
       "      <td>1119064</td>\n",
       "      <td>1026432</td>\n",
       "      <td>1264906</td>\n",
       "      <td>1135687</td>\n",
       "      <td>1278359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>1.1319</td>\n",
       "      <td>1.0866</td>\n",
       "      <td>1.1637</td>\n",
       "      <td>1.2003</td>\n",
       "      <td>1.1966</td>\n",
       "      <td>1.1983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1        1        1        1        1        1\n",
       "Intercept   0.0101   0.0116   0.0128   0.0095   0.0123   0.0109\n",
       "            (4.01)   (4.78)   (5.28)   (3.99)   (5.30)   (4.56)\n",
       "Bias       -0.2142  -0.5143  -0.6188   0.0038  -0.0890  -0.1758\n",
       "           (-1.78)  (-5.17)  (-6.81)   (0.10)  (-4.60)  (-3.35)\n",
       "N          1024253  1119064  1026432  1264906  1135687  1278359\n",
       "R2          1.1319   1.0866   1.1637   1.2003   1.1966   1.1983"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fama-macbeth Regression\n",
    "# Panel A of Table C.3: Replication with look-ahead bias\n",
    "results = []\n",
    "for idx in ['Q1','Q2','Q3','A1','A2','Avg']:\n",
    "    df_tmp = df[['YearMonth','bh1m',f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']].dropna().copy()\n",
    "    df_tmp[[f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']] = df_tmp.groupby('YearMonth',group_keys=False)[[f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']]\\\n",
    "                         .transform(lambda x: x.clip(x.quantile(0.01),x.quantile(0.99)))\n",
    "    rlt = utils.fama_macbeth(df_tmp,\n",
    "                   formula_list = [f'bh1m~BE_{idx}_wLAB'],\n",
    "                   time_id='YearMonth', lags=12, stars=False)\n",
    "    rlt = rlt.rename(index={f'BE_{idx}_wLAB':'Bias'})\n",
    "    results.append(rlt)\n",
    "\n",
    "results = pd.concat(results,axis=1)\n",
    "results.to_clipboard()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.01)</td>\n",
       "      <td>(4.15)</td>\n",
       "      <td>(4.20)</td>\n",
       "      <td>(3.99)</td>\n",
       "      <td>(4.17)</td>\n",
       "      <td>(3.91)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bias</th>\n",
       "      <td>-0.2142</td>\n",
       "      <td>-0.0859</td>\n",
       "      <td>-0.0574</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>-0.0075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-1.78)</td>\n",
       "      <td>(-0.78)</td>\n",
       "      <td>(-0.56)</td>\n",
       "      <td>(0.10)</td>\n",
       "      <td>(0.68)</td>\n",
       "      <td>(-0.15)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>1024253</td>\n",
       "      <td>1119064</td>\n",
       "      <td>1026432</td>\n",
       "      <td>1264906</td>\n",
       "      <td>1135687</td>\n",
       "      <td>1278359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>1.1319</td>\n",
       "      <td>1.0569</td>\n",
       "      <td>1.0787</td>\n",
       "      <td>1.2003</td>\n",
       "      <td>1.1296</td>\n",
       "      <td>1.1443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1        1        1        1        1        1\n",
       "Intercept   0.0101   0.0100   0.0099   0.0095   0.0095   0.0093\n",
       "            (4.01)   (4.15)   (4.20)   (3.99)   (4.17)   (3.91)\n",
       "Bias       -0.2142  -0.0859  -0.0574   0.0038   0.0135  -0.0075\n",
       "           (-1.78)  (-0.78)  (-0.56)   (0.10)   (0.68)  (-0.15)\n",
       "N          1024253  1119064  1026432  1264906  1135687  1278359\n",
       "R2          1.1319   1.0569   1.0787   1.2003   1.1296   1.1443"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Panel B of Table C.3: Replication without look-ahead bias\n",
    "results = []\n",
    "for idx in ['Q1','Q2','Q3','A1','A2','Avg']:\n",
    "    df_tmp = df[['YearMonth','bh1m',f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']].dropna().copy()\n",
    "    df_tmp[[f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']] = df_tmp.groupby('YearMonth',group_keys=False)[[f'BE_{idx}_wLAB',f'BE_{idx}_woLAB']]\\\n",
    "                         .transform(lambda x: x.clip(x.quantile(0.01),x.quantile(0.99)))\n",
    "    rlt = utils.fama_macbeth(df_tmp,\n",
    "                   formula_list = [f'bh1m~BE_{idx}_woLAB'],\n",
    "                   time_id='YearMonth', lags=12, stars=False)\n",
    "    rlt = rlt.rename(index={f'BE_{idx}_woLAB':'Bias'})\n",
    "    results.append(rlt)\n",
    "results = pd.concat(results,axis=1)\n",
    "results.to_clipboard()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table C.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Q1_woLAB, Delete 345813 rows due to missing values, raw data 1370066 rows --> new data 1024253 rows\n",
      "Var:BE_Q2_woLAB, Delete 251002 rows due to missing values, raw data 1370066 rows --> new data 1119064 rows\n",
      "Var:BE_Q3_woLAB, Delete 343634 rows due to missing values, raw data 1370066 rows --> new data 1026432 rows\n",
      "Var:BE_A1_woLAB, Delete 105160 rows due to missing values, raw data 1370066 rows --> new data 1264906 rows\n",
      "Var:BE_A2_woLAB, Delete 234379 rows due to missing values, raw data 1370066 rows --> new data 1135687 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th></th>\n",
       "      <th>Q2</th>\n",
       "      <th></th>\n",
       "      <th>Q3</th>\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-0.39359</td>\n",
       "      <td>-1.375036</td>\n",
       "      <td>-0.373432</td>\n",
       "      <td>-1.231261</td>\n",
       "      <td>-0.211208</td>\n",
       "      <td>-0.707617</td>\n",
       "      <td>-0.206421</td>\n",
       "      <td>-0.704115</td>\n",
       "      <td>0.076461</td>\n",
       "      <td>0.261529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Q1                  Q2                  Q3                  A1  \\\n",
       "ones -0.39359 -1.375036 -0.373432 -1.231261 -0.211208 -0.707617 -0.206421   \n",
       "\n",
       "                      A2            \n",
       "ones -0.704115  0.076461  0.261529  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Horizon without look-ahead bias: raw return (Panel A)\n",
    "rlts = []\n",
    "for sort_var in ['BE_Q1_woLAB','BE_Q2_woLAB','BE_Q3_woLAB','BE_A1_woLAB','BE_A2_woLAB']:\n",
    "    _,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "    exposure = Factor_Exposure(vwret, num_level, 'YearMonth',\n",
    "                all_factor, {'Return':['ones'],\n",
    "              }, None,\n",
    "                regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    rlts.append(exposure)\n",
    "    # break\n",
    "rlts = pd.concat(rlts, axis=1)\n",
    "rlts.columns = ['Q1','','Q2','','Q3','','A1','','A2','']\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Q1_woLAB, Delete 345813 rows due to missing values, raw data 1370066 rows --> new data 1024253 rows\n",
      "Var:BE_Q2_woLAB, Delete 251002 rows due to missing values, raw data 1370066 rows --> new data 1119064 rows\n",
      "Var:BE_Q3_woLAB, Delete 343634 rows due to missing values, raw data 1370066 rows --> new data 1026432 rows\n",
      "Var:BE_A1_woLAB, Delete 105160 rows due to missing values, raw data 1370066 rows --> new data 1264906 rows\n",
      "Var:BE_A2_woLAB, Delete 234379 rows due to missing values, raw data 1370066 rows --> new data 1135687 rows\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th></th>\n",
       "      <th>Q2</th>\n",
       "      <th></th>\n",
       "      <th>Q3</th>\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-0.753351</td>\n",
       "      <td>-2.940689</td>\n",
       "      <td>-0.762855</td>\n",
       "      <td>-2.811598</td>\n",
       "      <td>-0.596096</td>\n",
       "      <td>-2.221598</td>\n",
       "      <td>-0.573444</td>\n",
       "      <td>-2.208524</td>\n",
       "      <td>-0.291606</td>\n",
       "      <td>-1.139636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mkt_RF</th>\n",
       "      <td>0.522915</td>\n",
       "      <td>6.884365</td>\n",
       "      <td>0.566031</td>\n",
       "      <td>7.069131</td>\n",
       "      <td>0.559438</td>\n",
       "      <td>6.760112</td>\n",
       "      <td>0.533472</td>\n",
       "      <td>6.657156</td>\n",
       "      <td>0.534989</td>\n",
       "      <td>7.102960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Q1                  Q2                  Q3                  A1  \\\n",
       "ones   -0.753351 -2.940689 -0.762855 -2.811598 -0.596096 -2.221598 -0.573444   \n",
       "Mkt_RF  0.522915  6.884365  0.566031  7.069131  0.559438  6.760112  0.533472   \n",
       "\n",
       "                        A2            \n",
       "ones   -2.208524 -0.291606 -1.139636  \n",
       "Mkt_RF  6.657156  0.534989  7.102960  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Horizon without look-ahead bias: CAPM (Panel B)\n",
    "rlts = []\n",
    "for sort_var in ['BE_Q1_woLAB','BE_Q2_woLAB','BE_Q3_woLAB','BE_A1_woLAB','BE_A2_woLAB']:\n",
    "    _,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "    exposure = Factor_Exposure(vwret, num_level, 'YearMonth',\n",
    "                all_factor, {'CAPM':['ones','Mkt_RF'],\n",
    "              }, None,\n",
    "                regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    rlts.append(exposure)\n",
    "    # break\n",
    "rlts = pd.concat(rlts, axis=1)\n",
    "rlts.columns = ['Q1','','Q2','','Q3','','A1','','A2','']\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Q1_woLAB, Delete 345813 rows due to missing values, raw data 1370066 rows --> new data 1024253 rows\n",
      "Var:BE_Q2_woLAB, Delete 251002 rows due to missing values, raw data 1370066 rows --> new data 1119064 rows\n",
      "Var:BE_Q3_woLAB, Delete 343634 rows due to missing values, raw data 1370066 rows --> new data 1026432 rows\n",
      "Var:BE_A1_woLAB, Delete 105160 rows due to missing values, raw data 1370066 rows --> new data 1264906 rows\n",
      "Var:BE_A2_woLAB, Delete 234379 rows due to missing values, raw data 1370066 rows --> new data 1135687 rows\n"
     ]
    },
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th></th>\n",
       "      <th>Q2</th>\n",
       "      <th></th>\n",
       "      <th>Q3</th>\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-0.799405</td>\n",
       "      <td>-3.511633</td>\n",
       "      <td>-0.873279</td>\n",
       "      <td>-3.655010</td>\n",
       "      <td>-0.695454</td>\n",
       "      <td>-2.922000</td>\n",
       "      <td>-0.633699</td>\n",
       "      <td>-2.743120</td>\n",
       "      <td>-0.366945</td>\n",
       "      <td>-1.632272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mkt_RF</th>\n",
       "      <td>0.453586</td>\n",
       "      <td>6.135621</td>\n",
       "      <td>0.532813</td>\n",
       "      <td>7.249786</td>\n",
       "      <td>0.521859</td>\n",
       "      <td>6.807603</td>\n",
       "      <td>0.469598</td>\n",
       "      <td>6.003183</td>\n",
       "      <td>0.475911</td>\n",
       "      <td>6.802615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>0.842193</td>\n",
       "      <td>9.014884</td>\n",
       "      <td>0.839519</td>\n",
       "      <td>7.619949</td>\n",
       "      <td>0.823600</td>\n",
       "      <td>7.149205</td>\n",
       "      <td>0.863882</td>\n",
       "      <td>7.963840</td>\n",
       "      <td>0.895775</td>\n",
       "      <td>7.280442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HML</th>\n",
       "      <td>0.374807</td>\n",
       "      <td>3.055212</td>\n",
       "      <td>0.651214</td>\n",
       "      <td>5.890111</td>\n",
       "      <td>0.600262</td>\n",
       "      <td>5.369495</td>\n",
       "      <td>0.440459</td>\n",
       "      <td>3.760076</td>\n",
       "      <td>0.512052</td>\n",
       "      <td>4.267743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Q1                  Q2                  Q3                  A1  \\\n",
       "ones   -0.799405 -3.511633 -0.873279 -3.655010 -0.695454 -2.922000 -0.633699   \n",
       "Mkt_RF  0.453586  6.135621  0.532813  7.249786  0.521859  6.807603  0.469598   \n",
       "SMB     0.842193  9.014884  0.839519  7.619949  0.823600  7.149205  0.863882   \n",
       "HML     0.374807  3.055212  0.651214  5.890111  0.600262  5.369495  0.440459   \n",
       "\n",
       "                        A2            \n",
       "ones   -2.743120 -0.366945 -1.632272  \n",
       "Mkt_RF  6.003183  0.475911  6.802615  \n",
       "SMB     7.963840  0.895775  7.280442  \n",
       "HML     3.760076  0.512052  4.267743  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Horizon without look-ahead bias: FF3 (Panel C)\n",
    "rlts = []\n",
    "for sort_var in ['BE_Q1_woLAB','BE_Q2_woLAB','BE_Q3_woLAB','BE_A1_woLAB','BE_A2_woLAB']:\n",
    "    _,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "    exposure = Factor_Exposure(vwret, num_level, 'YearMonth',\n",
    "                all_factor, {\n",
    "                    # 'CAPM':['ones','Mkt_RF'],\n",
    "               'FF3':['ones','Mkt_RF','SMB','HML'],\n",
    "            #    'FF5':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA'],\n",
    "              }, None,\n",
    "                regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    rlts.append(exposure)\n",
    "    # break\n",
    "rlts = pd.concat(rlts, axis=1)\n",
    "rlts.columns = ['Q1','','Q2','','Q3','','A1','','A2','']\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Q1_woLAB, Delete 345813 rows due to missing values, raw data 1370066 rows --> new data 1024253 rows\n",
      "Var:BE_Q2_woLAB, Delete 251002 rows due to missing values, raw data 1370066 rows --> new data 1119064 rows\n",
      "Var:BE_Q3_woLAB, Delete 343634 rows due to missing values, raw data 1370066 rows --> new data 1026432 rows\n",
      "Var:BE_A1_woLAB, Delete 105160 rows due to missing values, raw data 1370066 rows --> new data 1264906 rows\n",
      "Var:BE_A2_woLAB, Delete 234379 rows due to missing values, raw data 1370066 rows --> new data 1135687 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th></th>\n",
       "      <th>Q2</th>\n",
       "      <th></th>\n",
       "      <th>Q3</th>\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ones</th>\n",
       "      <td>-0.324637</td>\n",
       "      <td>-1.340735</td>\n",
       "      <td>-0.489497</td>\n",
       "      <td>-1.813603</td>\n",
       "      <td>-0.289217</td>\n",
       "      <td>-1.095059</td>\n",
       "      <td>-0.186789</td>\n",
       "      <td>-0.721783</td>\n",
       "      <td>-0.062197</td>\n",
       "      <td>-0.247885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mkt_RF</th>\n",
       "      <td>0.294791</td>\n",
       "      <td>4.548778</td>\n",
       "      <td>0.404366</td>\n",
       "      <td>5.357903</td>\n",
       "      <td>0.382339</td>\n",
       "      <td>5.000063</td>\n",
       "      <td>0.319362</td>\n",
       "      <td>4.248418</td>\n",
       "      <td>0.372559</td>\n",
       "      <td>4.960869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>0.570303</td>\n",
       "      <td>5.435434</td>\n",
       "      <td>0.620231</td>\n",
       "      <td>5.190865</td>\n",
       "      <td>0.612485</td>\n",
       "      <td>4.929681</td>\n",
       "      <td>0.612421</td>\n",
       "      <td>5.071398</td>\n",
       "      <td>0.729656</td>\n",
       "      <td>5.471903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HML</th>\n",
       "      <td>0.712360</td>\n",
       "      <td>4.923879</td>\n",
       "      <td>0.924650</td>\n",
       "      <td>6.832774</td>\n",
       "      <td>0.913938</td>\n",
       "      <td>6.639143</td>\n",
       "      <td>0.763372</td>\n",
       "      <td>5.091116</td>\n",
       "      <td>0.738422</td>\n",
       "      <td>4.726030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMW</th>\n",
       "      <td>-0.922994</td>\n",
       "      <td>-5.865790</td>\n",
       "      <td>-0.744778</td>\n",
       "      <td>-4.264139</td>\n",
       "      <td>-0.732050</td>\n",
       "      <td>-3.954745</td>\n",
       "      <td>-0.856835</td>\n",
       "      <td>-5.047145</td>\n",
       "      <td>-0.569930</td>\n",
       "      <td>-3.025561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMA</th>\n",
       "      <td>-0.341515</td>\n",
       "      <td>-1.465084</td>\n",
       "      <td>-0.278139</td>\n",
       "      <td>-1.040012</td>\n",
       "      <td>-0.382185</td>\n",
       "      <td>-1.427190</td>\n",
       "      <td>-0.340183</td>\n",
       "      <td>-1.298105</td>\n",
       "      <td>-0.254332</td>\n",
       "      <td>-0.954854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Q1                  Q2                  Q3                  A1  \\\n",
       "ones   -0.324637 -1.340735 -0.489497 -1.813603 -0.289217 -1.095059 -0.186789   \n",
       "Mkt_RF  0.294791  4.548778  0.404366  5.357903  0.382339  5.000063  0.319362   \n",
       "SMB     0.570303  5.435434  0.620231  5.190865  0.612485  4.929681  0.612421   \n",
       "HML     0.712360  4.923879  0.924650  6.832774  0.913938  6.639143  0.763372   \n",
       "RMW    -0.922994 -5.865790 -0.744778 -4.264139 -0.732050 -3.954745 -0.856835   \n",
       "CMA    -0.341515 -1.465084 -0.278139 -1.040012 -0.382185 -1.427190 -0.340183   \n",
       "\n",
       "                        A2            \n",
       "ones   -0.721783 -0.062197 -0.247885  \n",
       "Mkt_RF  4.248418  0.372559  4.960869  \n",
       "SMB     5.071398  0.729656  5.471903  \n",
       "HML     5.091116  0.738422  4.726030  \n",
       "RMW    -5.047145 -0.569930 -3.025561  \n",
       "CMA    -1.298105 -0.254332 -0.954854  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Horizon without look-ahead bias: FF5 (Panel D)\n",
    "rlts = []\n",
    "for sort_var in ['BE_Q1_woLAB','BE_Q2_woLAB','BE_Q3_woLAB','BE_A1_woLAB','BE_A2_woLAB']:\n",
    "    _,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "    exposure = Factor_Exposure(vwret, num_level, 'YearMonth',\n",
    "                all_factor, {\n",
    "                    # 'CAPM':['ones','Mkt_RF'],\n",
    "            #    'FF3':['ones','Mkt_RF','SMB','HML'],\n",
    "               'FF5':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA'],\n",
    "              }, None,\n",
    "                regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    rlts.append(exposure)\n",
    "    # break\n",
    "rlts = pd.concat(rlts, axis=1)\n",
    "rlts.columns = ['Q1','','Q2','','Q3','','A1','','A2','']\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Q1_woLAB, Delete 345813 rows due to missing values, raw data 1370066 rows --> new data 1024253 rows\n",
      "Var:BE_Q2_woLAB, Delete 251002 rows due to missing values, raw data 1370066 rows --> new data 1119064 rows\n",
      "Var:BE_Q3_woLAB, Delete 343634 rows due to missing values, raw data 1370066 rows --> new data 1026432 rows\n",
      "Var:BE_A1_woLAB, Delete 105160 rows due to missing values, raw data 1370066 rows --> new data 1264906 rows\n",
      "Var:BE_A2_woLAB, Delete 234379 rows due to missing values, raw data 1370066 rows --> new data 1135687 rows\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
       "      <th></th>\n",
       "      <th>Q2</th>\n",
       "      <th></th>\n",
       "      <th>Q3</th>\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FFC6</th>\n",
       "      <td>-0.024919</td>\n",
       "      <td>-0.120396</td>\n",
       "      <td>-0.088486</td>\n",
       "      <td>-0.465680</td>\n",
       "      <td>0.118435</td>\n",
       "      <td>0.661571</td>\n",
       "      <td>0.186389</td>\n",
       "      <td>0.917355</td>\n",
       "      <td>0.319211</td>\n",
       "      <td>1.746313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HXZ-q4</th>\n",
       "      <td>-0.035521</td>\n",
       "      <td>-0.147432</td>\n",
       "      <td>-0.199583</td>\n",
       "      <td>-0.703866</td>\n",
       "      <td>0.003803</td>\n",
       "      <td>0.013784</td>\n",
       "      <td>0.145007</td>\n",
       "      <td>0.556162</td>\n",
       "      <td>0.276219</td>\n",
       "      <td>1.057817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HMXZ-q5</th>\n",
       "      <td>0.035565</td>\n",
       "      <td>0.140271</td>\n",
       "      <td>-0.050602</td>\n",
       "      <td>-0.168914</td>\n",
       "      <td>-0.009534</td>\n",
       "      <td>-0.032870</td>\n",
       "      <td>0.195037</td>\n",
       "      <td>0.716725</td>\n",
       "      <td>0.259274</td>\n",
       "      <td>0.966348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SY</th>\n",
       "      <td>-0.050644</td>\n",
       "      <td>-0.207046</td>\n",
       "      <td>0.035831</td>\n",
       "      <td>0.133758</td>\n",
       "      <td>0.114113</td>\n",
       "      <td>0.436086</td>\n",
       "      <td>0.234130</td>\n",
       "      <td>0.854248</td>\n",
       "      <td>0.408804</td>\n",
       "      <td>1.500669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHS</th>\n",
       "      <td>0.097708</td>\n",
       "      <td>0.300879</td>\n",
       "      <td>0.108753</td>\n",
       "      <td>0.306621</td>\n",
       "      <td>0.303931</td>\n",
       "      <td>0.877188</td>\n",
       "      <td>0.350925</td>\n",
       "      <td>1.024387</td>\n",
       "      <td>0.581792</td>\n",
       "      <td>1.688658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Q1                  Q2                  Q3                  A1  \\\n",
       "FFC6    -0.024919 -0.120396 -0.088486 -0.465680  0.118435  0.661571  0.186389   \n",
       "HXZ-q4  -0.035521 -0.147432 -0.199583 -0.703866  0.003803  0.013784  0.145007   \n",
       "HMXZ-q5  0.035565  0.140271 -0.050602 -0.168914 -0.009534 -0.032870  0.195037   \n",
       "SY      -0.050644 -0.207046  0.035831  0.133758  0.114113  0.436086  0.234130   \n",
       "DHS      0.097708  0.300879  0.108753  0.306621  0.303931  0.877188  0.350925   \n",
       "\n",
       "                         A2            \n",
       "FFC6     0.917355  0.319211  1.746313  \n",
       "HXZ-q4   0.556162  0.276219  1.057817  \n",
       "HMXZ-q5  0.716725  0.259274  0.966348  \n",
       "SY       0.854248  0.408804  1.500669  \n",
       "DHS      1.024387  0.581792  1.688658  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alphas under alternative factor models (Panel E)\n",
    "rlts = []\n",
    "\n",
    "for sort_var in ['BE_Q1_woLAB','BE_Q2_woLAB','BE_Q3_woLAB','BE_A1_woLAB','BE_A2_woLAB']:\n",
    "    _,vwret = utils.SingleSort(df,\n",
    "                             'PERMNO', 'YearMonth', sort_var, 'bh1m', num_level, 'ME', quantile_filter=None)\n",
    "    exposure = Factor_Exposure(vwret, num_level, 'YearMonth',\n",
    "                all_factor, {\n",
    "               'FFC6':['ones','Mkt_RF','SMB', 'HML', 'RMW', 'CMA','MOM'],\n",
    "               'HXZ':['ones','R_MKT','R_ME','R_IA','R_ROE'],\n",
    "               'HMXZ':['ones','R_MKT','R_ME','R_IA','R_ROE','R_EG'],\n",
    "               'SY':['ones','Mkt_RF','SMB_SY','MGMT', 'PERF'],\n",
    "               'DHS':['ones','Mkt_RF','PEAD', 'FIN'],\n",
    "              }, None,\n",
    "                regressor_order = ['ones','Mkt_RF', 'SMB', 'HML', 'RMW', 'CMA'])\n",
    "    rlt = exposure.loc['ones',:]\n",
    "    rlt = pd.DataFrame(rlt.values.reshape(-1,2))\n",
    "    rlt.index = ['FFC6','HXZ-q4','HMXZ-q5','SY','DHS']\n",
    "    rlts.append(rlt)\n",
    "    # break\n",
    "rlts = pd.concat(rlts, axis=1)\n",
    "rlts.columns = ['Q1','','Q2','','Q3','','A1','','A2','']\n",
    "rlts.to_clipboard()\n",
    "rlts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table D.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _col_params(model):\n",
    "    res = pd.DataFrame()\n",
    "    res_se = pd.DataFrame()\n",
    "    other_info = {}\n",
    "    def find_stars(p):\n",
    "        if p <= 0.01:\n",
    "            return '***'\n",
    "        elif (p > 0.01) & (p <= 0.05):\n",
    "            return '**'\n",
    "        elif (p > 0.05) & (p <= 0.10):\n",
    "            return '*'\n",
    "        elif p > 0.1:\n",
    "            return ''\n",
    "\n",
    "    res['coef.'] = model.params.round(3) # + model.pvalues.map(find_stars) #+ find_stars(model.pvalues)\n",
    "    res_se['t-stats'] = model.tstats.round(2)\n",
    "\n",
    "    other_info['N'] = model.nobs\n",
    "    other_info['R2'] = model.rsquared\n",
    "    return res,res_se,other_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_col(results, regressor_order = None):\n",
    "    res_all, res_se_all, other_info_all = [], [], []\n",
    "\n",
    "    for x in results:\n",
    "        res,res_se,other_info = _col_params(x)\n",
    "        res_all.append(res)\n",
    "        res_se_all.append(res_se)\n",
    "        other_info_all.append(other_info)\n",
    "\n",
    "    # cols = [_col_params(x) for x in results]\n",
    "    col_names = [str(i) for i in range(1,len(res_all)+1)]\n",
    "\n",
    "    res_all = pd.concat(res_all, axis=1)\n",
    "    res_se_all = pd.concat(res_se_all, axis=1)\n",
    "    other_info_all = pd.DataFrame(other_info_all).T\n",
    "\n",
    "    res_all.columns = col_names\n",
    "    res_se_all.columns = col_names\n",
    "    other_info_all.columns = col_names\n",
    "\n",
    "    if regressor_order:\n",
    "        res_all = res_all.loc[regressor_order,:]\n",
    "        res_se_all = res_se_all.loc[regressor_order,:]\n",
    "    else:\n",
    "        regressor_order = res_all.index\n",
    "\n",
    "    result = pd.concat([res_all.reset_index(drop=True),\n",
    "                        res_se_all.reset_index(drop=True)]).sort_index(kind='merge')\n",
    "\n",
    "    result.index = [item for elem in regressor_order for item in (elem, \"\")]\n",
    "\n",
    "    result = pd.concat([result, other_info_all])\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>0.91</td>\n",
       "      <td>5.63</td>\n",
       "      <td>-3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_q1</th>\n",
       "      <td>1.03</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>25.55</td>\n",
       "      <td>22.77</td>\n",
       "      <td>4.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_q2</th>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-13.09</td>\n",
       "      <td>-14.47</td>\n",
       "      <td>-1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_q1</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-1.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_q2</th>\n",
       "      <td>0.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>13.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q1_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q2_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.63</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q1_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q2_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>888806.00</td>\n",
       "      <td>888806.00</td>\n",
       "      <td>888806.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1          2          3\n",
       "Intercept         0.00       0.01      -0.00\n",
       "                  0.91       5.63      -3.80\n",
       "EPS_ana_q1        1.03       1.15       0.28\n",
       "                 25.55      22.77       4.93\n",
       "EPS_ana_q2       -0.68      -0.90      -0.06\n",
       "                -13.09     -14.47      -1.44\n",
       "Forecast_q1      -0.05        NaN        NaN\n",
       "                 -1.23        NaN        NaN\n",
       "Forecast_q2       0.73        NaN        NaN\n",
       "                 13.30        NaN        NaN\n",
       "RF_q1_wLAB         NaN      -0.21        NaN\n",
       "                   NaN      -4.07        NaN\n",
       "RF_q2_wLAB         NaN       0.99        NaN\n",
       "                   NaN      14.63        NaN\n",
       "RF_q1_woLAB        NaN        NaN       0.73\n",
       "                   NaN        NaN      12.85\n",
       "RF_q2_woLAB        NaN        NaN       0.06\n",
       "                   NaN        NaN       1.38\n",
       "N            888806.00  888806.00  888806.00\n",
       "R2                0.84       0.85       0.83"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "######################################################################\n",
    "## To replicate this Table, we need earnings forecast data from BHL ##\n",
    "######################################################################\n",
    "## Reciver BHL's earnings forecast from bias\n",
    "df['Forecast_q1'] = df['EPS_ana_q1'] - df['Q1_bias']*df['prc_l1']\n",
    "df['Forecast_q2'] = df['EPS_ana_q2'] - df['Q2_bias']*df['prc_l1']\n",
    "df['Forecast_q3'] = df['EPS_ana_q3'] - df['Q3_bias']*df['prc_l1']\n",
    "\n",
    "df['Forecast_y1'] = df['EPS_ana_y1'] - df['A1_bias']*df['prc_l1']\n",
    "df['Forecast_y2'] = df['EPS_ana_y2'] - df['A2_bias']*df['prc_l1']\n",
    "\n",
    "## Q1\n",
    "df_ = df.dropna(subset = ['EPS_true_q1','Forecast_q1','Forecast_q2',\n",
    "                          'EPS_true_l1_q1','EPS_ana_q1','EPS_ana_q2',\n",
    "                          'RF_q1_woLAB','RF_q2_woLAB',\n",
    "                          'RF_q1_wLAB','RF_q2_wLAB',\n",
    "\n",
    "                        ])\n",
    "mdl1 = PanelOLS.from_formula('EPS_true_q1~1 + EPS_ana_q1+EPS_ana_q2+Forecast_q1+Forecast_q2',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl2 = PanelOLS.from_formula('EPS_true_q1~1 + EPS_ana_q1+EPS_ana_q2+RF_q1_wLAB+RF_q2_wLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl3 = PanelOLS.from_formula('EPS_true_q1~1 + EPS_ana_q1+EPS_ana_q2+RF_q1_woLAB+RF_q2_woLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "rlt = summary_col([mdl1, mdl2, mdl3]).round(2)\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-3.70</td>\n",
       "      <td>2.13</td>\n",
       "      <td>-7.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_q2</th>\n",
       "      <td>1.09</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>26.97</td>\n",
       "      <td>28.94</td>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_q3</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-20.28</td>\n",
       "      <td>-21.96</td>\n",
       "      <td>-0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_q2</th>\n",
       "      <td>-0.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-5.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_q3</th>\n",
       "      <td>0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>20.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q2_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q3_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.57</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q2_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_q3_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>992138.00</td>\n",
       "      <td>992138.00</td>\n",
       "      <td>992138.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1          2          3\n",
       "Intercept        -0.01       0.00      -0.01\n",
       "                 -3.70       2.13      -7.21\n",
       "EPS_ana_q2        1.09       1.16       0.46\n",
       "                 26.97      28.94       7.32\n",
       "EPS_ana_q3       -0.75      -0.93      -0.03\n",
       "                -20.28     -21.96      -0.94\n",
       "Forecast_q2      -0.24        NaN        NaN\n",
       "                 -5.32        NaN        NaN\n",
       "Forecast_q3       0.92        NaN        NaN\n",
       "                 20.17        NaN        NaN\n",
       "RF_q2_wLAB         NaN      -0.38        NaN\n",
       "                   NaN      -8.59        NaN\n",
       "RF_q3_wLAB         NaN       1.16        NaN\n",
       "                   NaN      22.57        NaN\n",
       "RF_q2_woLAB        NaN        NaN       0.54\n",
       "                   NaN        NaN       8.36\n",
       "RF_q3_woLAB        NaN        NaN       0.04\n",
       "                   NaN        NaN       1.09\n",
       "N            992138.00  992138.00  992138.00\n",
       "R2                0.82       0.82       0.78"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Q2\n",
    "df_ = df.dropna(subset = ['EPS_true_q2','Forecast_q2','Forecast_q3',\n",
    "                          'EPS_true_l1_q1','EPS_ana_q2','EPS_ana_q3',\n",
    "                          'RF_q2_woLAB','RF_q3_woLAB',\n",
    "                          'RF_q2_wLAB','RF_q3_wLAB',\n",
    "\n",
    "                        ])\n",
    "\n",
    "mdl1 = PanelOLS.from_formula('EPS_true_q2~1 + EPS_ana_q2 + EPS_ana_q3 + Forecast_q2+Forecast_q3',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl2 = PanelOLS.from_formula('EPS_true_q2~1 + EPS_ana_q2 + EPS_ana_q3 + RF_q2_wLAB + RF_q3_wLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl3 = PanelOLS.from_formula('EPS_true_q2~1 + EPS_ana_q2 + EPS_ana_q3 + RF_q2_woLAB+RF_q3_woLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "rlt = summary_col([mdl1, mdl2, mdl3]).round(2)\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-11.52</td>\n",
       "      <td>-2.63</td>\n",
       "      <td>-5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_y1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>28.93</td>\n",
       "      <td>26.80</td>\n",
       "      <td>13.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPS_ana_y2</th>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-18.51</td>\n",
       "      <td>-22.52</td>\n",
       "      <td>-6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_y1</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>-9.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forecast_y2</th>\n",
       "      <td>0.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>30.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_y1_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.61</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_y2_wLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_y1_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_y2_woLAB</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>999625.00</td>\n",
       "      <td>999625.00</td>\n",
       "      <td>999625.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1          2          3\n",
       "Intercept        -0.08      -0.02      -0.04\n",
       "                -11.52      -2.63      -5.34\n",
       "EPS_ana_y1        1.04       1.22       0.61\n",
       "                 28.93      26.80      13.53\n",
       "EPS_ana_y2       -0.28      -0.46      -0.12\n",
       "                -18.51     -22.52      -6.58\n",
       "Forecast_y1      -0.30        NaN        NaN\n",
       "                 -9.28        NaN        NaN\n",
       "Forecast_y2       0.58        NaN        NaN\n",
       "                 30.19        NaN        NaN\n",
       "RF_y1_wLAB         NaN      -0.41        NaN\n",
       "                   NaN      -8.61        NaN\n",
       "RF_y2_wLAB         NaN       0.68        NaN\n",
       "                   NaN      24.67        NaN\n",
       "RF_y1_woLAB        NaN        NaN       0.50\n",
       "                   NaN        NaN      11.28\n",
       "RF_y2_woLAB        NaN        NaN       0.02\n",
       "                   NaN        NaN       1.22\n",
       "N            999625.00  999625.00  999625.00\n",
       "R2                0.92       0.91       0.89"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Y1\n",
    "df_ = df.dropna(subset = ['EPS_true_y1','Forecast_y1','Forecast_y2',\n",
    "                          'EPS_true_l1_y1','EPS_ana_y1','EPS_ana_y2',\n",
    "                          'RF_y1_woLAB','RF_y2_woLAB',\n",
    "                          'RF_y1_wLAB','RF_y2_wLAB',\n",
    "\n",
    "                        ])\n",
    "mdl1 = PanelOLS.from_formula('EPS_true_y1~1 + EPS_ana_y1  + EPS_ana_y2 + Forecast_y1+Forecast_y2',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl2 = PanelOLS.from_formula('EPS_true_y1~1 + EPS_ana_y1 + EPS_ana_y2+ RF_y1_wLAB+RF_y2_wLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "mdl3 = PanelOLS.from_formula('EPS_true_y1~1 + EPS_ana_y1 + EPS_ana_y2+RF_y1_woLAB+RF_y2_woLAB',\n",
    "                             data=df_.set_index(['permno','YearMonth'])).fit(cov_type='clustered', cluster_time=True, cluster_entity=True, low_memory=True)\n",
    "rlt = summary_col([mdl1, mdl2, mdl3]).round(2)\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table E.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_parquet('./results/df_test.parquet')\n",
    "# Rational Benchmark w/ LAB\n",
    "df['RF_q1_wLAB_prc'] = df['RF_q1_wLAB']/df['prc_l1']\n",
    "df['RF_q2_wLAB_prc'] = df['RF_q2_wLAB']/df['prc_l1']\n",
    "df['RF_q3_wLAB_prc'] = df['RF_q3_wLAB']/df['prc_l1']\n",
    "df['RF_y1_wLAB_prc'] = df['RF_y1_wLAB']/df['prc_l1']\n",
    "df['RF_y2_wLAB_prc'] = df['RF_y2_wLAB']/df['prc_l1']\n",
    "nonNA = (~df[['RF_q1_wLAB_prc','RF_q2_wLAB_prc','RF_q3_wLAB_prc',\n",
    "                   'RF_y1_wLAB_prc','RF_y2_wLAB_prc']].isna()).sum(axis=1)\n",
    "df['RF_wLAB_Avg_prc'] = np.where(nonNA>1,\n",
    "                             df[['RF_q1_wLAB_prc','RF_q2_wLAB_prc','RF_q3_wLAB_prc',\n",
    "                                       'RF_y1_wLAB_prc','RF_y2_wLAB_prc']].mean(axis=1,),np.nan)\n",
    "\n",
    "# Rational Benchmark w/o LAB\n",
    "df['RF_q1_woLAB_prc'] = df['RF_q1_woLAB']/df['prc_l1']\n",
    "df['RF_q2_woLAB_prc'] = df['RF_q2_woLAB']/df['prc_l1']\n",
    "df['RF_q3_woLAB_prc'] = df['RF_q3_woLAB']/df['prc_l1']\n",
    "df['RF_y1_woLAB_prc'] = df['RF_y1_woLAB']/df['prc_l1']\n",
    "df['RF_y2_woLAB_prc'] = df['RF_y2_woLAB']/df['prc_l1']\n",
    "nonNA = (~df[['RF_q1_woLAB_prc','RF_q2_woLAB_prc','RF_q3_woLAB_prc',\n",
    "                   'RF_y1_woLAB_prc','RF_y2_woLAB_prc']].isna()).sum(axis=1)\n",
    "df['RF_woLAB_Avg_prc'] = np.where(nonNA>1,\n",
    "                             df[['RF_q1_woLAB_prc','RF_q2_woLAB_prc','RF_q3_woLAB_prc',\n",
    "                                       'RF_y1_woLAB_prc','RF_y2_woLAB_prc']].mean(axis=1,),np.nan)\n",
    "\n",
    "# Analyst Forecast\n",
    "df['EPS_ana_q1_prc'] = df['EPS_ana_q1']/df['prc_l1']\n",
    "df['EPS_ana_q2_prc'] = df['EPS_ana_q2']/df['prc_l1']\n",
    "df['EPS_ana_q3_prc'] = df['EPS_ana_q3']/df['prc_l1']\n",
    "df['EPS_ana_y1_prc'] = df['EPS_ana_y1']/df['prc_l1']\n",
    "df['EPS_ana_y2_prc'] = df['EPS_ana_y2']/df['prc_l1']\n",
    "\n",
    "nonNA = (~df[['EPS_ana_q1_prc','EPS_ana_q2_prc','EPS_ana_q3_prc',\n",
    "                   'EPS_ana_y1_prc','EPS_ana_y2_prc']].isna()).sum(axis=1)\n",
    "df['EPS_ana_Avg_prc'] = np.where(nonNA>1,\n",
    "                             df[['EPS_ana_q1_prc','EPS_ana_q2_prc','EPS_ana_q3_prc',\n",
    "                                       'EPS_ana_y1_prc','EPS_ana_y2_prc']].mean(axis=1,),np.nan)\n",
    "\n",
    "\n",
    "# Last EPS\n",
    "df['neg_EPS_true_l1_q1_prc'] = -df['EPS_true_l1_q1'] / df['prc_l1']\n",
    "df['neg_EPS_true_l1_y1_prc'] = -df['EPS_true_l1_y1'] / df['prc_l1']\n",
    "\n",
    "# Previous 12 month avg Bias\n",
    "df['BE_Avg_wLAB_12m'] = df.groupby('gvkey')['BE_Avg_wLAB'].rolling(12).mean().reset_index(level=0,drop=True)\n",
    "df['BE_Avg_woLAB_12m'] = df.groupby('gvkey')['BE_Avg_woLAB'].rolling(12).mean().reset_index(level=0,drop=True)\n",
    "\n",
    "# Negative of Rational Benchmark\n",
    "df['neg_RF_wLAB_Avg_prc_12m'] = -df.groupby('gvkey')['RF_wLAB_Avg_prc'].rolling(12).mean().reset_index(level=0,drop=True)\n",
    "df['neg_RF_woLAB_Avg_prc_12m'] = -df.groupby('gvkey')['RF_woLAB_Avg_prc'].rolling(12).mean().reset_index(level=0,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Net Stock Issuances and Conditional Biases\n",
    "# Split-adjusted Shares outstanding from compustat\n",
    "compa = pd.read_parquet('../data/WRDS/compa.parquet')\n",
    "compa['gvkey'] = compa['gvkey'].astype(float)\n",
    "compa['datadate'] = compa['datadate'] + MonthEnd(0)\n",
    "compa['nsi'] = np.nan_to_num(compa['nsi'], nan=np.nan,\n",
    "                             posinf=np.nan, neginf=np.nan)\n",
    "\n",
    "# Firm-Year Panel\n",
    "df_nsi = df.merge(compa, left_on=['gvkey','YearMonth'], right_on=['gvkey','datadate'])\n",
    "df_nsi = df_nsi[(df_nsi['fyear']>=1986) & (df_nsi['fyear']<=2019)]\n",
    "df_nsi['ones'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg_wLAB_12m, Delete 0 rows due to missing values, raw data 84782 rows --> new data 84782 rows\n",
      "Var:BE_Avg_woLAB_12m, Delete 0 rows due to missing values, raw data 84782 rows --> new data 84782 rows\n",
      "Var:neg_EPS_true_l1_y1_prc, Delete 0 rows due to missing values, raw data 84782 rows --> new data 84782 rows\n",
      "Var:neg_RF_wLAB_Avg_prc_12m, Delete 0 rows due to missing values, raw data 84782 rows --> new data 84782 rows\n",
      "Var:neg_RF_woLAB_Avg_prc_12m, Delete 0 rows due to missing values, raw data 84782 rows --> new data 84782 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE_Avg_wLAB_12m</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.66)</td>\n",
       "      <td>(2.45)</td>\n",
       "      <td>(2.50)</td>\n",
       "      <td>(5.22)</td>\n",
       "      <td>(5.32)</td>\n",
       "      <td>(4.62)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE_Avg_woLAB_12m</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.73)</td>\n",
       "      <td>(2.84)</td>\n",
       "      <td>(3.13)</td>\n",
       "      <td>(5.42)</td>\n",
       "      <td>(5.14)</td>\n",
       "      <td>(4.39)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_EPS_true_l1_y1_prc</th>\n",
       "      <td>0.012</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(2.29)</td>\n",
       "      <td>(1.13)</td>\n",
       "      <td>(1.45)</td>\n",
       "      <td>(7.58)</td>\n",
       "      <td>(6.42)</td>\n",
       "      <td>(5.40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_RF_wLAB_Avg_prc_12m</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.28)</td>\n",
       "      <td>(1.25)</td>\n",
       "      <td>(2.78)</td>\n",
       "      <td>(7.16)</td>\n",
       "      <td>(6.35)</td>\n",
       "      <td>(5.30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_RF_woLAB_Avg_prc_12m</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.52)</td>\n",
       "      <td>(1.24)</td>\n",
       "      <td>(2.73)</td>\n",
       "      <td>(5.68)</td>\n",
       "      <td>(6.28)</td>\n",
       "      <td>(4.92)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               1       2       3       4       5     H-L\n",
       "BE_Avg_wLAB_12m            0.007   0.016   0.018   0.035   0.080   0.072\n",
       "                          (1.66)  (2.45)  (2.50)  (5.22)  (5.32)  (4.62)\n",
       "BE_Avg_woLAB_12m           0.009   0.015   0.015   0.034   0.077   0.068\n",
       "                          (1.73)  (2.84)  (3.13)  (5.42)  (5.14)  (4.39)\n",
       "neg_EPS_true_l1_y1_prc     0.012   0.005   0.007   0.025   0.099   0.087\n",
       "                          (2.29)  (1.13)  (1.45)  (7.58)  (6.42)  (5.40)\n",
       "neg_RF_wLAB_Avg_prc_12m    0.007   0.008   0.011   0.031   0.106   0.099\n",
       "                          (1.28)  (1.25)  (2.78)  (7.16)  (6.35)  (5.30)\n",
       "neg_RF_woLAB_Avg_prc_12m   0.010   0.006   0.011   0.034   0.104   0.094\n",
       "                          (1.52)  (1.24)  (2.73)  (5.68)  (6.28)  (4.92)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Panel A and C of Table E.1 ###\n",
    "factor_dict = {'Ret':['ones'],\n",
    "              }\n",
    "sort_var = 'BE_Avg_wLAB_12m'\n",
    "num_level = 5\n",
    "\n",
    "results = []\n",
    "for v in ['BE_Avg_wLAB_12m', 'BE_Avg_woLAB_12m',\n",
    "          'neg_EPS_true_l1_y1_prc',\n",
    "          'neg_RF_wLAB_Avg_prc_12m', 'neg_RF_woLAB_Avg_prc_12m']:\n",
    "    _,vwret = utils.SingleSort(df_nsi.dropna(subset=['BE_Avg_wLAB_12m','BE_Avg_woLAB_12m','nsi']),\n",
    "                                'gvkey', 'fyear', v, 'nsi', num_level, 'ME', quantile_filter=None)\n",
    "    result = utils.SingleSort_RetAna(_,vwret/100,'fyear',factor_data=None,\n",
    "                                    factor_dict=factor_dict,lag=1,float_format='%.3f')\n",
    "    result.index = [v, '']\n",
    "    results.append(result)\n",
    "rlt = pd.concat(results,axis=0)\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.035</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(9.76)</td>\n",
       "      <td>(10.28)</td>\n",
       "      <td>(9.65)</td>\n",
       "      <td>(10.24)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE_Avg_wLAB_12m</th>\n",
       "      <td>0.644</td>\n",
       "      <td>0.018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(4.41)</td>\n",
       "      <td>(0.18)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_RF_wLAB_Avg_prc_12m</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>(5.39)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE_Avg_woLAB_12m</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(4.49)</td>\n",
       "      <td>(0.34)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_RF_woLAB_Avg_prc_12m</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(5.40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>84782</td>\n",
       "      <td>84782</td>\n",
       "      <td>84782</td>\n",
       "      <td>84782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>2.691</td>\n",
       "      <td>10.326</td>\n",
       "      <td>3.224</td>\n",
       "      <td>10.451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               1        2       3        4\n",
       "Intercept                  0.035    0.043   0.034    0.043\n",
       "                          (9.76)  (10.28)  (9.65)  (10.24)\n",
       "BE_Avg_wLAB_12m            0.644    0.018     NaN      NaN\n",
       "                          (4.41)   (0.18)     NaN      NaN\n",
       "neg_RF_wLAB_Avg_prc_12m      NaN    0.426     NaN      NaN\n",
       "                             NaN   (5.39)     NaN      NaN\n",
       "BE_Avg_woLAB_12m             NaN      NaN   0.766    0.037\n",
       "                             NaN      NaN  (4.49)   (0.34)\n",
       "neg_RF_woLAB_Avg_prc_12m     NaN      NaN     NaN    0.420\n",
       "                             NaN      NaN     NaN   (5.40)\n",
       "N                          84782    84782   84782    84782\n",
       "R2                         2.691   10.326   3.224   10.451"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Panel B of Table E.1 ###\n",
    "rlt = utils.fama_macbeth(df_nsi.dropna(subset = ['nsi','BE_Avg_wLAB_12m']),\n",
    "                   formula_list = ['nsi~BE_Avg_wLAB_12m',\n",
    "                                   'nsi~BE_Avg_wLAB_12m + neg_RF_wLAB_Avg_prc_12m',\n",
    "                                   'nsi~BE_Avg_woLAB_12m',\n",
    "                                   'nsi~BE_Avg_woLAB_12m + neg_RF_woLAB_Avg_prc_12m',\n",
    "                                  ],\n",
    "                   time_id='fyear', lags=1, stars=False, float_format='%.3f')\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table E.2 & E.3 & E.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Conditional Bias and Anomalies\n",
    "variable_names = [\n",
    "    \"EarningsSurprise\", \"AnnouncementReturn\",    \"Mom12m\",    \"BM\",\n",
    "    \"EquityDuration\",    \"EP\",    \"CF\",    \"NetPayoutYield\",    \"AssetGrowth\",\n",
    "    \"NOA\",    \"InvestPPEInv\",    \"grcapx\",    \"CompEquIss\",    \"ShareIss1Y\",\n",
    "    \"ChInv\",    \"InvGrowth\",    \"Accruals\",    \"PctAcc\",    \"PctTotAcc\",\n",
    "    \"GP\",    \"RoE\",    \"NumEarnIncrease\", \"roaq\",    \"OrgCap\",    \"AdExp\",\n",
    "    \"RD\",    \"OPLeverage\"\n",
    "]\n",
    "\n",
    "osap = pd.read_csv('../data/Other/signed_predictors_dl_wide_v1.4.1.csv',\n",
    "                   usecols=['permno','yyyymm'] + variable_names,\n",
    "                   )\n",
    "osap['YearMonth'] = pd.to_datetime(osap['yyyymm'], format='%Y%m') + MonthEnd(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## So Bias\n",
    "forecast_So = pd.read_parquet('../data/Results/So_eps_AF.parquet')\n",
    "# forecast_So = pd.read_parquet('../data_BK_250308/Results/So_eps_AF.parquet')\n",
    "forecast_So = forecast_So.merge(df[['permno','YearMonth','prc_l1']], on=['permno','YearMonth'])\n",
    "\n",
    "forecast_So['BE_Q1'] = (forecast_So['AF_q1'] - forecast_So['So_q1']) / forecast_So['prc_l1']\n",
    "forecast_So['BE_Q2'] = (forecast_So['AF_q2'] - forecast_So['So_q2']) / forecast_So['prc_l1']\n",
    "forecast_So['BE_Q3'] = (forecast_So['AF_q3'] - forecast_So['So_q3']) / forecast_So['prc_l1']\n",
    "forecast_So['BE_A1_So'] = (forecast_So['AF_y1'] - forecast_So['So_y1']) / forecast_So['prc_l1']\n",
    "forecast_So['BE_A2'] = (forecast_So['AF_y2'] - forecast_So['So_y2']) / forecast_So['prc_l1']\n",
    "nonNA = (~forecast_So[['BE_Q1','BE_Q2','BE_Q3','BE_A1_So','BE_A2']].isna()).sum(axis=1)\n",
    "\n",
    "forecast_So['BE_Avg_So'] = np.where(nonNA>1,\n",
    "                                    forecast_So[['BE_Q1','BE_Q2','BE_Q3','BE_A1_So','BE_A2']].mean(axis=1,),np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df.merge(forecast_So[['permno','YearMonth','BE_Avg_So','BE_A1_So']], on=['permno','YearMonth'])\n",
    "df_ = df_.merge(osap, on=['permno','YearMonth'])\n",
    "df_.dropna(subset=['BE_Avg_woLAB','BE_Avg_So'], inplace=True)\n",
    "df_.reset_index(inplace=True)\n",
    "\n",
    "## Anomaly Score\n",
    "for v in variable_names:\n",
    "    df_sub = df_[['permno','YearMonth',v]].dropna(subset=[v])\n",
    "    n_stk = df_sub.groupby('YearMonth').size()\n",
    "    df_sub = df_sub[df_sub.YearMonth.isin(n_stk[n_stk >= 100].index)]\n",
    "    df_[f'{v}_decile'] = df_sub.groupby('YearMonth')[v].transform(lambda x: pd.qcut(x.rank(pct=True,\n",
    "                                                                  method='first'),\n",
    "                                                                   10,\n",
    "                                                           labels=False) + 1)\n",
    "\n",
    "decile_var = [f'{v}_decile' for v in variable_names]\n",
    "df_['anomaly_score'] = np.where((~df_[decile_var].isna()).sum(axis=1) >= 10,\n",
    "                                 df_[decile_var].mean(axis=1), np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var1:BE_Avg_wLAB Var2:anomaly_score; Delete 90146 rows due to missing values, raw data 1278395 --> new data 1188249; Number of Periods: 408\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>port_var2</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_var1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>95</td>\n",
       "      <td>72</td>\n",
       "      <td>63</td>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>290</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "port_var2   1    2    3    4    5    6    7    8    9    10\n",
       "port_var1                                                  \n",
       "1           38   44   48   53   58   63   67   70   70   67\n",
       "2           34   49   56   59   62   65   66   65   63   59\n",
       "3           50   58   60   61   60   60   58   58   57   56\n",
       "4           71   66   61   58   56   53   52   52   52   57\n",
       "5           95   72   63   58   52   49   47   45   46   50\n",
       "All        290  291  291  291  291  291  291  291  291  291"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Panel B of Table E.2\n",
    "df_,vwret,result_wLAB = utils.DoubleSort2(df_, 'YearMonth', 'BE_Avg_wLAB', 'anomaly_score',\n",
    "                                      5, 10, 'bh1m', 'ME', lag=12, dependent=False,\n",
    "                                      )\n",
    "\n",
    "num_stocks = df_.groupby(['YearMonth','port_var1','port_var2']).size().unstack().groupby(level=1).mean()\n",
    "num_stocks.loc['All',:] = num_stocks.sum(axis=0)\n",
    "num_stocks = num_stocks.astype(int)\n",
    "num_stocks.to_clipboard()\n",
    "num_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var1:BE_Avg_woLAB Var2:anomaly_score; Delete 0 rows due to missing values, raw data 1188249 --> new data 1188249; Number of Periods: 408\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>port_var2</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>port_var1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>48</td>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>61</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>64</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>93</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>290</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "port_var2   1    2    3    4    5    6    7    8    9    10\n",
       "port_var1                                                  \n",
       "1           40   48   52   55   61   64   67   68   65   58\n",
       "2           36   50   56   59   62   64   64   64   63   58\n",
       "3           49   56   58   60   59   59   58   59   58   59\n",
       "4           70   64   60   57   55   53   52   52   54   60\n",
       "5           93   71   62   57   52   49   47   46   48   54\n",
       "All        290  291  291  291  291  291  291  291  291  291"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Panel C of Table E.2\n",
    "df_,vwret,result_woLAB = utils.DoubleSort2(df_, 'YearMonth', 'BE_Avg_woLAB', 'anomaly_score',\n",
    "                                      5, 10, 'bh1m', 'ME', lag=12, dependent=False,\n",
    "                                      )\n",
    "\n",
    "num_stocks = df_.groupby(['YearMonth','port_var1','port_var2']).size().unstack().groupby(level=1).mean()\n",
    "num_stocks.loc['All',:] = num_stocks.sum(axis=0)\n",
    "num_stocks = num_stocks.astype(int)\n",
    "num_stocks.to_clipboard()\n",
    "num_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var1:EPS_ana_Avg_prc Var2:anomaly_score; Delete 0 rows due to missing values, raw data 1188249 --> new data 1188249; Number of Periods: 408\n"
     ]
    },
    {
     "data": {
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       "      <th>port_var2</th>\n",
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       "      <th>port_var1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>142</td>\n",
       "      <td>97</td>\n",
       "      <td>73</td>\n",
       "      <td>58</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>35</td>\n",
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       "      <td>28</td>\n",
       "      <td>26</td>\n",
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       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>56</td>\n",
       "      <td>53</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>47</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>66</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>290</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "port_var2   1    2    3    4    5    6    7    8    9    10\n",
       "port_var1                                                  \n",
       "1          142   97   73   58   47   41   35   30   28   26\n",
       "2           62   71   70   65   61   56   53   49   46   44\n",
       "3           33   48   56   60   64   65   65   65   63   61\n",
       "4           25   38   47   55   62   66   70   72   73   71\n",
       "5           27   36   44   51   56   61   66   72   78   88\n",
       "All        290  291  291  291  291  291  291  291  291  291"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Panel D of Table E.2\n",
    "# By AF\n",
    "df_,vwret,result = utils.DoubleSort2(df_, 'YearMonth', 'EPS_ana_Avg_prc', 'anomaly_score',\n",
    "                                      5, 10, 'bh1m', 'ME', lag=12, dependent=False,\n",
    "                                      )\n",
    "\n",
    "num_stocks = df_.groupby(['YearMonth','port_var1','port_var2']).size().unstack().groupby(level=1).mean()\n",
    "num_stocks.loc['All',:] = num_stocks.sum(axis=0)\n",
    "num_stocks = num_stocks.astype(int)\n",
    "num_stocks.to_clipboard()\n",
    "num_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var1:neg_RF_woLAB_Avg_prc Var2:anomaly_score; Delete 0 rows due to missing values, raw data 1188249 --> new data 1188249; Number of Periods: 408\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>port_var2</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>9</th>\n",
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       "    <tr>\n",
       "      <th>port_var1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>49</td>\n",
       "      <td>56</td>\n",
       "      <td>64</td>\n",
       "      <td>71</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>37</td>\n",
       "      <td>47</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>78</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>45</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>140</td>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>57</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>290</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "port_var2   1    2    3    4    5    6    7    8    9    10\n",
       "port_var1                                                  \n",
       "1           18   29   40   49   56   64   71   78   84   88\n",
       "2           21   37   47   56   63   67   70   72   72   70\n",
       "3           35   53   60   62   65   64   63   61   58   57\n",
       "4           75   78   73   65   58   52   48   45   42   43\n",
       "5          140   91   69   57   47   41   36   33   32   32\n",
       "All        290  291  291  291  291  291  291  291  291  291"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Panel E of Table E.2\n",
    "# By earnings forecast\n",
    "df_['neg_RF_wLAB_Avg_prc'] = -df_['RF_wLAB_Avg_prc']\n",
    "df_['neg_RF_woLAB_Avg_prc'] = -df_['RF_woLAB_Avg_prc']\n",
    "df_,vwret,result = utils.DoubleSort2(df_, 'YearMonth', 'neg_RF_woLAB_Avg_prc', 'anomaly_score',\n",
    "                                      5, 10, 'bh1m', 'ME', lag=12, dependent=False,\n",
    "                                      )\n",
    "\n",
    "num_stocks = df_.groupby(['YearMonth','port_var1','port_var2']).size().unstack().groupby(level=1).mean()\n",
    "num_stocks.loc['All',:] = num_stocks.sum(axis=0)\n",
    "num_stocks = num_stocks.astype(int)\n",
    "num_stocks.to_clipboard()\n",
    "num_stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.39</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(3.22)</td>\n",
       "      <td>(5.64)</td>\n",
       "      <td>(4.52)</td>\n",
       "      <td>(6.10)</td>\n",
       "      <td>(5.99)</td>\n",
       "      <td>(6.61)</td>\n",
       "      <td>(6.79)</td>\n",
       "      <td>(8.86)</td>\n",
       "      <td>(7.93)</td>\n",
       "      <td>(7.65)</td>\n",
       "      <td>(0.44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.83)</td>\n",
       "      <td>(1.45)</td>\n",
       "      <td>(3.54)</td>\n",
       "      <td>(4.44)</td>\n",
       "      <td>(5.16)</td>\n",
       "      <td>(4.72)</td>\n",
       "      <td>(5.48)</td>\n",
       "      <td>(4.86)</td>\n",
       "      <td>(5.08)</td>\n",
       "      <td>(5.66)</td>\n",
       "      <td>(1.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.26)</td>\n",
       "      <td>(0.73)</td>\n",
       "      <td>(0.72)</td>\n",
       "      <td>(2.29)</td>\n",
       "      <td>(1.51)</td>\n",
       "      <td>(3.39)</td>\n",
       "      <td>(3.38)</td>\n",
       "      <td>(3.24)</td>\n",
       "      <td>(3.08)</td>\n",
       "      <td>(4.66)</td>\n",
       "      <td>(2.91)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-1.55)</td>\n",
       "      <td>(-0.16)</td>\n",
       "      <td>(-0.01)</td>\n",
       "      <td>(0.54)</td>\n",
       "      <td>(0.86)</td>\n",
       "      <td>(2.53)</td>\n",
       "      <td>(1.32)</td>\n",
       "      <td>(1.36)</td>\n",
       "      <td>(2.67)</td>\n",
       "      <td>(1.78)</td>\n",
       "      <td>(3.93)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.05</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-2.04)</td>\n",
       "      <td>(-1.60)</td>\n",
       "      <td>(-0.44)</td>\n",
       "      <td>(-1.16)</td>\n",
       "      <td>(0.19)</td>\n",
       "      <td>(-0.35)</td>\n",
       "      <td>(0.42)</td>\n",
       "      <td>(0.26)</td>\n",
       "      <td>(1.33)</td>\n",
       "      <td>(1.86)</td>\n",
       "      <td>(5.32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>-2.44</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>-1.37</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>-1.45</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-5.14)</td>\n",
       "      <td>(-5.61)</td>\n",
       "      <td>(-3.24)</td>\n",
       "      <td>(-5.23)</td>\n",
       "      <td>(-3.33)</td>\n",
       "      <td>(-3.56)</td>\n",
       "      <td>(-2.46)</td>\n",
       "      <td>(-3.24)</td>\n",
       "      <td>(-1.96)</td>\n",
       "      <td>(-2.45)</td>\n",
       "      <td>(4.28)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1        2        3        4        5        6        7        8  \\\n",
       "1       1.39     1.63     1.34     1.44     1.45     1.47     1.32     1.58   \n",
       "      (3.22)   (5.64)   (4.52)   (6.10)   (5.99)   (6.61)   (6.79)   (8.86)   \n",
       "2       0.65     0.50     0.83     0.96     1.14     0.99     1.10     1.01   \n",
       "      (1.83)   (1.45)   (3.54)   (4.44)   (5.16)   (4.72)   (5.48)   (4.86)   \n",
       "3      -0.11     0.23     0.23     0.64     0.44     0.93     0.91     0.75   \n",
       "     (-0.26)   (0.73)   (0.72)   (2.29)   (1.51)   (3.39)   (3.38)   (3.24)   \n",
       "4      -0.74    -0.06    -0.00     0.20     0.30     0.68     0.44     0.45   \n",
       "     (-1.55)  (-0.16)  (-0.01)   (0.54)   (0.86)   (2.53)   (1.32)   (1.36)   \n",
       "5      -1.05    -0.87    -0.23    -0.48     0.08    -0.18     0.22     0.13   \n",
       "     (-2.04)  (-1.60)  (-0.44)  (-1.16)   (0.19)  (-0.35)   (0.42)   (0.26)   \n",
       "H-L    -2.44    -2.51    -1.57    -1.93    -1.37    -1.65    -1.11    -1.45   \n",
       "     (-5.14)  (-5.61)  (-3.24)  (-5.23)  (-3.33)  (-3.56)  (-2.46)  (-3.24)   \n",
       "\n",
       "           9       10     H-L  \n",
       "1       1.51     1.54    0.15  \n",
       "      (7.93)   (7.65)  (0.44)  \n",
       "2       0.97     1.19    0.54  \n",
       "      (5.08)   (5.66)  (1.60)  \n",
       "3       0.76     0.99    1.10  \n",
       "      (3.08)   (4.66)  (2.91)  \n",
       "4       0.84     0.63    1.37  \n",
       "      (2.67)   (1.78)  (3.93)  \n",
       "5       0.65     0.73    1.78  \n",
       "      (1.33)   (1.86)  (5.32)  \n",
       "H-L    -0.86    -0.81    1.63  \n",
       "     (-1.96)  (-2.45)  (4.28)  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#################\n",
    "### Table E.3 ###\n",
    "#################\n",
    "\n",
    "# Panel B of Table E.3\n",
    "result_wLAB.to_clipboard()\n",
    "result_wLAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>H-L</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.45</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.44)</td>\n",
       "      <td>(3.19)</td>\n",
       "      <td>(3.26)</td>\n",
       "      <td>(4.41)</td>\n",
       "      <td>(4.49)</td>\n",
       "      <td>(4.43)</td>\n",
       "      <td>(5.68)</td>\n",
       "      <td>(6.53)</td>\n",
       "      <td>(6.18)</td>\n",
       "      <td>(4.90)</td>\n",
       "      <td>(1.29)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.65</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.04)</td>\n",
       "      <td>(1.90)</td>\n",
       "      <td>(1.87)</td>\n",
       "      <td>(4.24)</td>\n",
       "      <td>(4.80)</td>\n",
       "      <td>(5.28)</td>\n",
       "      <td>(4.63)</td>\n",
       "      <td>(5.59)</td>\n",
       "      <td>(4.74)</td>\n",
       "      <td>(4.89)</td>\n",
       "      <td>(2.05)</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.32)</td>\n",
       "      <td>(1.58)</td>\n",
       "      <td>(2.71)</td>\n",
       "      <td>(3.26)</td>\n",
       "      <td>(2.53)</td>\n",
       "      <td>(5.10)</td>\n",
       "      <td>(4.46)</td>\n",
       "      <td>(3.63)</td>\n",
       "      <td>(5.19)</td>\n",
       "      <td>(6.36)</td>\n",
       "      <td>(4.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.65)</td>\n",
       "      <td>(1.83)</td>\n",
       "      <td>(1.54)</td>\n",
       "      <td>(2.31)</td>\n",
       "      <td>(2.93)</td>\n",
       "      <td>(3.65)</td>\n",
       "      <td>(3.81)</td>\n",
       "      <td>(3.63)</td>\n",
       "      <td>(4.61)</td>\n",
       "      <td>(4.65)</td>\n",
       "      <td>(4.63)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.49</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.29</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.51)</td>\n",
       "      <td>(0.20)</td>\n",
       "      <td>(1.48)</td>\n",
       "      <td>(1.28)</td>\n",
       "      <td>(2.01)</td>\n",
       "      <td>(1.98)</td>\n",
       "      <td>(2.48)</td>\n",
       "      <td>(2.45)</td>\n",
       "      <td>(2.97)</td>\n",
       "      <td>(4.23)</td>\n",
       "      <td>(6.96)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>-0.87</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.84</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-1.64)</td>\n",
       "      <td>(-2.08)</td>\n",
       "      <td>(-0.28)</td>\n",
       "      <td>(-0.94)</td>\n",
       "      <td>(-0.44)</td>\n",
       "      <td>(0.00)</td>\n",
       "      <td>(0.57)</td>\n",
       "      <td>(-0.09)</td>\n",
       "      <td>(0.76)</td>\n",
       "      <td>(2.24)</td>\n",
       "      <td>(4.66)</td>\n",
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      ],
      "text/plain": [
       "           1        2        3        4        5       6       7        8  \\\n",
       "1       0.60     0.91     0.84     0.99     1.12    0.99    1.09     1.20   \n",
       "      (1.44)   (3.19)   (3.26)   (4.41)   (4.49)  (4.43)  (5.68)   (6.53)   \n",
       "2       0.39     0.57     0.50     0.94     1.01    1.10    1.06     1.10   \n",
       "      (1.04)   (1.90)   (1.87)   (4.24)   (4.80)  (5.28)  (4.63)   (5.59)   \n",
       "3      -0.14     0.47     0.69     0.91     0.79    1.33    1.07     0.85   \n",
       "     (-0.32)   (1.58)   (2.71)   (3.26)   (2.53)  (5.10)  (4.46)   (3.63)   \n",
       "4      -0.30     0.68     0.57     0.77     0.91    1.08    1.17     1.11   \n",
       "     (-0.65)   (1.83)   (1.54)   (2.31)   (2.93)  (3.65)  (3.81)   (3.63)   \n",
       "5      -0.27     0.10     0.72     0.59     0.93    0.99    1.38     1.16   \n",
       "     (-0.51)   (0.20)   (1.48)   (1.28)   (2.01)  (1.98)  (2.48)   (2.45)   \n",
       "H-L    -0.87    -0.81    -0.13    -0.40    -0.20    0.00    0.28    -0.04   \n",
       "     (-1.64)  (-2.08)  (-0.28)  (-0.94)  (-0.44)  (0.00)  (0.57)  (-0.09)   \n",
       "\n",
       "          9      10     H-L  \n",
       "1      1.15    1.05    0.45  \n",
       "     (6.18)  (4.90)  (1.29)  \n",
       "2      0.97    1.04    0.65  \n",
       "     (4.74)  (4.89)  (2.05)  \n",
       "3      1.15    1.51    1.65  \n",
       "     (5.19)  (6.36)  (4.04)  \n",
       "4      1.57    1.32    1.62  \n",
       "     (4.61)  (4.65)  (4.63)  \n",
       "5      1.49    2.02    2.29  \n",
       "     (2.97)  (4.23)  (6.96)  \n",
       "H-L    0.33    0.97    1.84  \n",
       "     (0.76)  (2.24)  (4.66)  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Panel C of Table E.3\n",
    "result_woLAB.to_clipboard()\n",
    "result_woLAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var1:BE_Avg_So Var2:anomaly_score; Delete 0 rows due to missing values, raw data 1188249 --> new data 1188249; Number of Periods: 408\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.74</td>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.17)</td>\n",
       "      <td>(2.64)</td>\n",
       "      <td>(2.54)</td>\n",
       "      <td>(4.40)</td>\n",
       "      <td>(4.24)</td>\n",
       "      <td>(4.59)</td>\n",
       "      <td>(4.91)</td>\n",
       "      <td>(5.98)</td>\n",
       "      <td>(5.90)</td>\n",
       "      <td>(5.70)</td>\n",
       "      <td>(2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.24)</td>\n",
       "      <td>(2.09)</td>\n",
       "      <td>(2.13)</td>\n",
       "      <td>(4.36)</td>\n",
       "      <td>(3.88)</td>\n",
       "      <td>(4.89)</td>\n",
       "      <td>(5.46)</td>\n",
       "      <td>(5.61)</td>\n",
       "      <td>(5.23)</td>\n",
       "      <td>(5.82)</td>\n",
       "      <td>(2.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.44)</td>\n",
       "      <td>(1.41)</td>\n",
       "      <td>(2.65)</td>\n",
       "      <td>(3.45)</td>\n",
       "      <td>(5.66)</td>\n",
       "      <td>(4.73)</td>\n",
       "      <td>(4.73)</td>\n",
       "      <td>(4.45)</td>\n",
       "      <td>(4.39)</td>\n",
       "      <td>(4.55)</td>\n",
       "      <td>(3.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.93)</td>\n",
       "      <td>(0.36)</td>\n",
       "      <td>(1.73)</td>\n",
       "      <td>(2.10)</td>\n",
       "      <td>(2.94)</td>\n",
       "      <td>(3.55)</td>\n",
       "      <td>(3.47)</td>\n",
       "      <td>(4.00)</td>\n",
       "      <td>(4.42)</td>\n",
       "      <td>(4.23)</td>\n",
       "      <td>(4.44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.16</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-0.51)</td>\n",
       "      <td>(0.62)</td>\n",
       "      <td>(1.91)</td>\n",
       "      <td>(2.11)</td>\n",
       "      <td>(2.99)</td>\n",
       "      <td>(2.55)</td>\n",
       "      <td>(2.45)</td>\n",
       "      <td>(2.67)</td>\n",
       "      <td>(3.34)</td>\n",
       "      <td>(4.24)</td>\n",
       "      <td>(5.88)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(-1.65)</td>\n",
       "      <td>(-1.40)</td>\n",
       "      <td>(0.45)</td>\n",
       "      <td>(-0.16)</td>\n",
       "      <td>(0.55)</td>\n",
       "      <td>(0.27)</td>\n",
       "      <td>(0.63)</td>\n",
       "      <td>(0.11)</td>\n",
       "      <td>(1.08)</td>\n",
       "      <td>(2.03)</td>\n",
       "      <td>(3.93)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1        2       3        4       5       6       7       8  \\\n",
       "1       0.46     0.80    0.71     1.01    1.11    1.07    1.07    1.11   \n",
       "      (1.17)   (2.64)  (2.54)   (4.40)  (4.24)  (4.59)  (4.91)  (5.98)   \n",
       "2       0.43     0.61    0.54     1.01    0.84    1.06    1.11    1.11   \n",
       "      (1.24)   (2.09)  (2.13)   (4.36)  (3.88)  (4.89)  (5.46)  (5.61)   \n",
       "3      -0.20     0.49    0.76     0.85    1.22    1.05    1.15    1.10   \n",
       "     (-0.44)   (1.41)  (2.65)   (3.45)  (5.66)  (4.73)  (4.73)  (4.45)   \n",
       "4      -0.45     0.13    0.63     0.74    0.99    1.15    1.05    1.19   \n",
       "     (-0.93)   (0.36)  (1.73)   (2.10)  (2.94)  (3.55)  (3.47)  (4.00)   \n",
       "5      -0.26     0.29    0.93     0.94    1.36    1.19    1.38    1.16   \n",
       "     (-0.51)   (0.62)  (1.91)   (2.11)  (2.99)  (2.55)  (2.45)  (2.67)   \n",
       "H-L    -0.72    -0.52    0.22    -0.07    0.26    0.12    0.31    0.05   \n",
       "     (-1.65)  (-1.40)  (0.45)  (-0.16)  (0.55)  (0.27)  (0.63)  (0.11)   \n",
       "\n",
       "          9      10     H-L  \n",
       "1      1.10    1.20    0.74  \n",
       "     (5.90)  (5.70)  (2.22)  \n",
       "2      1.05    1.11    0.68  \n",
       "     (5.23)  (5.82)  (2.21)  \n",
       "3      1.19    1.18    1.37  \n",
       "     (4.39)  (4.55)  (3.56)  \n",
       "4      1.48    1.27    1.72  \n",
       "     (4.42)  (4.23)  (4.44)  \n",
       "5      1.57    2.16    2.42  \n",
       "     (3.34)  (4.24)  (5.88)  \n",
       "H-L    0.47    0.96    1.68  \n",
       "     (1.08)  (2.03)  (3.93)  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Panel D of Table E.3\n",
    "# Sort by So (2013) model\n",
    "df_,vwret,result = utils.DoubleSort2(df_, 'YearMonth', 'BE_Avg_So', 'anomaly_score',\n",
    "                                      5, 10, 'bh1m', 'ME', lag=12, dependent=False,\n",
    "                                      )\n",
    "\n",
    "result.to_clipboard()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:anomaly_score, Delete 0 rows due to missing values, raw data 1188249 rows --> new data 1188249 rows\n",
      "Var:anomaly_score, Delete 0 rows due to missing values, raw data 1188249 rows --> new data 1188249 rows\n",
      "Var:anomaly_score, Delete 0 rows due to missing values, raw data 1188249 rows --> new data 1188249 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE_Avg_wLAB</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(6.46)</td>\n",
       "      <td>(6.10)</td>\n",
       "      <td>(6.02)</td>\n",
       "      <td>(5.93)</td>\n",
       "      <td>(5.49)</td>\n",
       "      <td>(5.48)</td>\n",
       "      <td>(5.50)</td>\n",
       "      <td>(5.37)</td>\n",
       "      <td>(5.26)</td>\n",
       "      <td>(4.92)</td>\n",
       "      <td>(-5.44)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE_Avg_woLAB</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(6.21)</td>\n",
       "      <td>(5.78)</td>\n",
       "      <td>(5.69)</td>\n",
       "      <td>(5.68)</td>\n",
       "      <td>(5.32)</td>\n",
       "      <td>(5.39)</td>\n",
       "      <td>(5.57)</td>\n",
       "      <td>(5.53)</td>\n",
       "      <td>(5.60)</td>\n",
       "      <td>(5.22)</td>\n",
       "      <td>(-4.53)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE_Avg_So</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(6.16)</td>\n",
       "      <td>(4.82)</td>\n",
       "      <td>(5.19)</td>\n",
       "      <td>(4.83)</td>\n",
       "      <td>(3.78)</td>\n",
       "      <td>(4.32)</td>\n",
       "      <td>(4.37)</td>\n",
       "      <td>(4.41)</td>\n",
       "      <td>(4.54)</td>\n",
       "      <td>(4.04)</td>\n",
       "      <td>(-5.20)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   1       2       3       4       5       6       7       8  \\\n",
       "BE_Avg_wLAB    0.008   0.005   0.005   0.004   0.004   0.004   0.003   0.003   \n",
       "              (6.46)  (6.10)  (6.02)  (5.93)  (5.49)  (5.48)  (5.50)  (5.37)   \n",
       "BE_Avg_woLAB   0.008   0.005   0.005   0.004   0.004   0.004   0.004   0.003   \n",
       "              (6.21)  (5.78)  (5.69)  (5.68)  (5.32)  (5.39)  (5.57)  (5.53)   \n",
       "BE_Avg_So      0.007   0.005   0.004   0.003   0.004   0.003   0.003   0.003   \n",
       "              (6.16)  (4.82)  (5.19)  (4.83)  (3.78)  (4.32)  (4.37)  (4.41)   \n",
       "\n",
       "                   9      10      H-L  \n",
       "BE_Avg_wLAB    0.003   0.004   -0.004  \n",
       "              (5.26)  (4.92)  (-5.44)  \n",
       "BE_Avg_woLAB   0.004   0.004   -0.003  \n",
       "              (5.60)  (5.22)  (-4.53)  \n",
       "BE_Avg_So      0.003   0.004   -0.003  \n",
       "              (4.54)  (4.04)  (-5.20)  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#################\n",
    "### Table E.4 ###\n",
    "#################\n",
    "factor_dict = {'Ret':['ones'],}\n",
    "sort_var = 'anomaly_score'\n",
    "num_level = 10\n",
    "results = []\n",
    "for agg_var in ['BE_Avg_wLAB','BE_Avg_woLAB','BE_Avg_So']:\n",
    "    _,vwret = utils.SingleSort(df_,\n",
    "                            'gvkey', 'YearMonth', sort_var, agg_var, num_level, 'ME', quantile_filter=None)\n",
    "    result = utils.SingleSort_RetAna(_,vwret/100,'YearMonth',factor_data=None,\n",
    "                                  factor_dict=factor_dict,lag=12,float_format='%.3f')\n",
    "    result.index = [agg_var, '']\n",
    "    results.append(result)\n",
    "rlt = pd.concat(results,axis=0)\n",
    "rlt.to_clipboard()\n",
    "rlt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################################################################\n",
    "## To replicate this figure, we need earnings forecast data from BHL ##\n",
    "#######################################################################\n",
    "# Load anomaly data from OSAP\n",
    "osap = pd.read_csv('../data/Other/signed_predictors_dl_wide_v1.4.1.csv',\n",
    "                #    nrows=10000\n",
    "                   )\n",
    "osap['YearMonth'] = pd.to_datetime(osap['yyyymm'], format='%Y%m') + MonthEnd(0)\n",
    "anomaly_list = list(osap.columns[2:-1]) + ['PRC', 'Size', 'STreversal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crsp monthly\n",
    "crsp = pd.read_parquet('../data/WRDS/crsp_m.parquet')\n",
    "crsp['ME'] = abs(crsp['prc'])*crsp['shrout']\n",
    "crsp.sort_values(by=['permno','YearMonth'], inplace=True)\n",
    "crsp['bh1m'] = crsp.groupby('permno')['retadj'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample Filter\n",
    "NYSE_20th = crsp[crsp['exchcd']==1].groupby('YearMonth')['ME'].quantile(0.2)\n",
    "crsp = crsp.merge(NYSE_20th, on='YearMonth', suffixes=('', '_NYSE20th'))\n",
    "crsp = crsp[crsp['ME'] >= crsp['ME_NYSE20th']].copy()\n",
    "crsp = crsp.merge(osap[['permno','YearMonth','BMdec']], on=['permno','YearMonth'], how='left')\n",
    "crsp = crsp[(crsp['prc'].abs() > 1)].copy()\n",
    "crsp = crsp[(crsp['YearMonth'] >= '1986-01-01') & (crsp['YearMonth'] <= '2019-12-31')].copy()\n",
    "\n",
    "# additional signals\n",
    "crsp['STreversal'] = -crsp['retadj']\n",
    "crsp['Size'] = -crsp['ME']\n",
    "crsp['PRC'] = -crsp['prc'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All signals\n",
    "df = crsp[['permno','YearMonth','ME','bh1m',\n",
    "           'STreversal','Size','PRC'\n",
    "           ]].merge(osap, on=['permno','YearMonth'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: AM\n",
      "1: AOP\n",
      "2: AbnormalAccruals\n",
      "3: Accruals\n",
      "4: AccrualsBM\n",
      "5: Activism1\n",
      "6: Activism2\n",
      "7: AdExp\n",
      "8: AgeIPO\n",
      "9: AnalystRevision\n",
      "10: AnalystValue\n",
      "11: AnnouncementReturn\n",
      "12: AssetGrowth\n",
      "13: BM\n",
      "14: BMdec\n",
      "15: BPEBM\n",
      "16: Beta\n",
      "17: BetaFP\n",
      "18: BetaLiquidityPS\n",
      "19: BetaTailRisk\n",
      "20: BidAskSpread\n",
      "21: BookLeverage\n",
      "22: BrandInvest\n",
      "23: CBOperProf\n",
      "24: CF\n",
      "25: CPVolSpread\n",
      "26: Cash\n",
      "27: CashProd\n",
      "28: ChAssetTurnover\n",
      "29: ChEQ\n",
      "30: ChForecastAccrual\n",
      "31: ChInv\n",
      "32: ChInvIA\n",
      "33: ChNAnalyst\n",
      "34: ChNNCOA\n",
      "35: ChNWC\n",
      "36: ChTax\n",
      "37: ChangeInRecommendation\n",
      "38: CitationsRD\n",
      "39: CompEquIss\n",
      "40: CompositeDebtIssuance\n",
      "41: ConsRecomm\n",
      "42: ConvDebt\n",
      "43: CoskewACX\n",
      "44: Coskewness\n",
      "45: CredRatDG\n",
      "46: CustomerMomentum\n",
      "47: DebtIssuance\n",
      "48: DelBreadth\n",
      "49: DelCOA\n",
      "50: DelCOL\n",
      "51: DelDRC\n",
      "52: DelEqu\n",
      "53: DelFINL\n",
      "54: DelLTI\n",
      "55: DelNetFin\n",
      "56: DivInit\n",
      "57: DivOmit\n",
      "58: DivSeason\n",
      "59: DivYieldST\n",
      "60: DolVol\n",
      "61: DownRecomm\n",
      "62: EBM\n",
      "63: EP\n",
      "64: EarnSupBig\n",
      "65: EarningsConsistency\n",
      "66: EarningsForecastDisparity\n",
      "67: EarningsStreak\n",
      "68: EarningsSurprise\n",
      "69: EntMult\n",
      "70: EquityDuration\n",
      "71: ExchSwitch\n",
      "72: ExclExp\n",
      "73: FEPS\n",
      "74: FR\n",
      "75: FirmAge\n",
      "76: FirmAgeMom\n",
      "77: ForecastDispersion\n",
      "78: Frontier\n",
      "79: GP\n",
      "80: Governance\n",
      "81: GrAdExp\n",
      "82: GrLTNOA\n",
      "83: GrSaleToGrInv\n",
      "84: GrSaleToGrOverhead\n",
      "85: Herf\n",
      "86: HerfAsset\n",
      "87: HerfBE\n",
      "88: High52\n",
      "89: IO_ShortInterest\n",
      "90: IdioVol3F\n",
      "91: IdioVolAHT\n",
      "92: Illiquidity\n",
      "93: IndIPO\n",
      "94: IndMom\n",
      "95: IndRetBig\n",
      "96: IntMom\n",
      "97: IntanBM\n",
      "98: IntanCFP\n",
      "99: IntanEP\n",
      "100: IntanSP\n",
      "101: InvGrowth\n",
      "102: InvestPPEInv\n",
      "103: Investment\n",
      "104: LRreversal\n",
      "105: Leverage\n",
      "106: MRreversal\n",
      "107: MS\n",
      "108: MaxRet\n",
      "109: MeanRankRevGrowth\n",
      "110: Mom12m\n",
      "111: Mom12mOffSeason\n",
      "112: Mom6m\n",
      "113: Mom6mJunk\n",
      "114: MomOffSeason\n",
      "115: MomOffSeason06YrPlus\n",
      "116: MomOffSeason11YrPlus\n",
      "117: MomOffSeason16YrPlus\n",
      "118: MomRev\n",
      "119: MomSeason\n",
      "120: MomSeason06YrPlus\n",
      "121: MomSeason11YrPlus\n",
      "122: MomSeason16YrPlus\n",
      "123: MomSeasonShort\n",
      "124: MomVol\n",
      "125: NOA\n",
      "126: NetDebtFinance\n",
      "127: NetDebtPrice\n",
      "128: NetEquityFinance\n",
      "129: NetPayoutYield\n",
      "130: NumEarnIncrease\n",
      "131: OPLeverage\n",
      "132: OScore\n",
      "133: OperProf\n",
      "134: OperProfRD\n",
      "135: OptionVolume1\n",
      "136: OptionVolume2\n",
      "137: OrderBacklog\n",
      "138: OrderBacklogChg\n",
      "139: OrgCap\n",
      "140: PS\n",
      "141: PatentsRD\n",
      "142: PayoutYield\n",
      "143: PctAcc\n",
      "144: PctTotAcc\n",
      "145: PredictedFE\n",
      "146: PriceDelayRsq\n",
      "147: PriceDelaySlope\n",
      "148: PriceDelayTstat\n",
      "149: ProbInformedTrading\n",
      "150: RD\n",
      "151: RDAbility\n",
      "152: RDIPO\n",
      "153: RDS\n",
      "154: RDcap\n",
      "155: REV6\n",
      "156: RIO_Disp\n",
      "157: RIO_MB\n",
      "158: RIO_Turnover\n",
      "159: RIO_Volatility\n",
      "160: RIVolSpread\n",
      "161: RealizedVol\n",
      "162: Recomm_ShortInterest\n",
      "163: ResidualMomentum\n",
      "164: ReturnSkew\n",
      "165: ReturnSkew3F\n",
      "166: RevenueSurprise\n",
      "167: RoE\n",
      "168: SP\n",
      "169: ShareIss1Y\n",
      "170: ShareIss5Y\n",
      "171: ShareRepurchase\n",
      "172: ShareVol\n",
      "173: ShortInterest\n",
      "174: SmileSlope\n",
      "175: Spinoff\n",
      "176: SurpriseRD\n",
      "177: Tax\n",
      "178: TotalAccruals\n",
      "179: TrendFactor\n",
      "180: UpRecomm\n",
      "181: VarCF\n",
      "182: VolMkt\n",
      "183: VolSD\n",
      "184: VolumeTrend\n",
      "185: XFIN\n",
      "186: betaVIX\n",
      "187: cfp\n",
      "188: dCPVolSpread\n",
      "189: dNoa\n",
      "190: dVolCall\n",
      "191: dVolPut\n",
      "192: fgr5yrLag\n",
      "193: grcapx\n",
      "194: grcapx3y\n",
      "195: hire\n",
      "196: iomom_cust\n",
      "197: iomom_supp\n",
      "198: realestate\n",
      "199: retConglomerate\n",
      "200: roaq\n",
      "201: sfe\n",
      "202: sinAlgo\n",
      "203: skew1\n",
      "204: std_turn\n",
      "205: tang\n",
      "206: zerotrade12M\n",
      "207: zerotrade1M\n",
      "208: zerotrade6M\n",
      "209: PRC\n",
      "210: Size\n",
      "211: STreversal\n"
     ]
    }
   ],
   "source": [
    "# anomaly filter\n",
    "filter_res = []\n",
    "for i,signal in enumerate(anomaly_list):\n",
    "    print(f\"{i}: {signal}\")\n",
    "    # Number of Unique Values\n",
    "    U_value = df[signal].nunique()\n",
    "\n",
    "    # Number of Observations in each YearMonth\n",
    "    aux_res = df[['YearMonth',signal]].dropna(subset=[signal]).groupby('YearMonth')[signal].size().reset_index(name='N')\n",
    "\n",
    "    # Average Number of Observations\n",
    "    N_row = aux_res.shape[0]\n",
    "    N_avg = aux_res['N'].mean()\n",
    "\n",
    "    filter_res.append({'Var': signal, 'U_value': U_value, 'N_row': N_row, 'N_avg': N_avg})\n",
    "    # break\n",
    "filter_res = pd.DataFrame(filter_res)\n",
    "filter_res = filter_res[(filter_res['U_value'] > 100) &\n",
    "                                (filter_res['N_row'] == 408) &\n",
    "                                (filter_res['N_avg'] > 500)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151, 4)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 151 signals\n",
    "filter_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:AM, Delete 65128 rows due to missing values, raw data 840957 rows --> new data 775829 rows\n",
      "Var:AOP, Delete 277242 rows due to missing values, raw data 840957 rows --> new data 563715 rows\n",
      "Var:AbnormalAccruals, Delete 190303 rows due to missing values, raw data 840957 rows --> new data 650654 rows\n",
      "Var:Accruals, Delete 113161 rows due to missing values, raw data 840957 rows --> new data 727796 rows\n",
      "Var:AdExp, Delete 568536 rows due to missing values, raw data 840957 rows --> new data 272421 rows\n",
      "Var:AnalystRevision, Delete 57997 rows due to missing values, raw data 840957 rows --> new data 782960 rows\n",
      "Var:AnalystValue, Delete 277242 rows due to missing values, raw data 840957 rows --> new data 563715 rows\n",
      "Var:AnnouncementReturn, Delete 41838 rows due to missing values, raw data 840957 rows --> new data 799119 rows\n",
      "Var:AssetGrowth, Delete 108510 rows due to missing values, raw data 840957 rows --> new data 732447 rows\n",
      "Var:BM, Delete 165901 rows due to missing values, raw data 840957 rows --> new data 675056 rows\n",
      "Var:BMdec, Delete 110979 rows due to missing values, raw data 840957 rows --> new data 729978 rows\n",
      "Var:BPEBM, Delete 71508 rows due to missing values, raw data 840957 rows --> new data 769449 rows\n",
      "Var:Beta, Delete 77269 rows due to missing values, raw data 840957 rows --> new data 763688 rows\n",
      "Var:BetaFP, Delete 128682 rows due to missing values, raw data 840957 rows --> new data 712275 rows\n",
      "Var:BetaLiquidityPS, Delete 129168 rows due to missing values, raw data 840957 rows --> new data 711789 rows\n",
      "Var:BetaTailRisk, Delete 228148 rows due to missing values, raw data 840957 rows --> new data 612809 rows\n",
      "Var:BidAskSpread, Delete 7669 rows due to missing values, raw data 840957 rows --> new data 833288 rows\n",
      "Var:BookLeverage, Delete 65219 rows due to missing values, raw data 840957 rows --> new data 775738 rows\n",
      "Var:CBOperProf, Delete 224101 rows due to missing values, raw data 840957 rows --> new data 616856 rows\n",
      "Var:CF, Delete 65128 rows due to missing values, raw data 840957 rows --> new data 775829 rows\n",
      "Var:Cash, Delete 152088 rows due to missing values, raw data 840957 rows --> new data 688869 rows\n",
      "Var:CashProd, Delete 70487 rows due to missing values, raw data 840957 rows --> new data 770470 rows\n",
      "Var:ChAssetTurnover, Delete 259902 rows due to missing values, raw data 840957 rows --> new data 581055 rows\n",
      "Var:ChEQ, Delete 130385 rows due to missing values, raw data 840957 rows --> new data 710572 rows\n",
      "Var:ChInv, Delete 108498 rows due to missing values, raw data 840957 rows --> new data 732459 rows\n",
      "Var:ChInvIA, Delete 126709 rows due to missing values, raw data 840957 rows --> new data 714248 rows\n",
      "Var:ChNNCOA, Delete 115853 rows due to missing values, raw data 840957 rows --> new data 725104 rows\n",
      "Var:ChNWC, Delete 113185 rows due to missing values, raw data 840957 rows --> new data 727772 rows\n",
      "Var:ChTax, Delete 117090 rows due to missing values, raw data 840957 rows --> new data 723867 rows\n",
      "Var:CompEquIss, Delete 259418 rows due to missing values, raw data 840957 rows --> new data 581539 rows\n",
      "Var:CompositeDebtIssuance, Delete 326143 rows due to missing values, raw data 840957 rows --> new data 514814 rows\n",
      "Var:CoskewACX, Delete 48880 rows due to missing values, raw data 840957 rows --> new data 792077 rows\n",
      "Var:Coskewness, Delete 49154 rows due to missing values, raw data 840957 rows --> new data 791803 rows\n",
      "Var:DelBreadth, Delete 26346 rows due to missing values, raw data 840957 rows --> new data 814611 rows\n",
      "Var:DelCOA, Delete 108498 rows due to missing values, raw data 840957 rows --> new data 732459 rows\n",
      "Var:DelCOL, Delete 113161 rows due to missing values, raw data 840957 rows --> new data 727796 rows\n",
      "Var:DelEqu, Delete 108598 rows due to missing values, raw data 840957 rows --> new data 732359 rows\n",
      "Var:DelFINL, Delete 115818 rows due to missing values, raw data 840957 rows --> new data 725139 rows\n",
      "Var:DelLTI, Delete 108498 rows due to missing values, raw data 840957 rows --> new data 732459 rows\n",
      "Var:DelNetFin, Delete 115818 rows due to missing values, raw data 840957 rows --> new data 725139 rows\n",
      "Var:DolVol, Delete 11721 rows due to missing values, raw data 840957 rows --> new data 829236 rows\n",
      "Var:EBM, Delete 71508 rows due to missing values, raw data 840957 rows --> new data 769449 rows\n",
      "Var:EP, Delete 183565 rows due to missing values, raw data 840957 rows --> new data 657392 rows\n",
      "Var:EarnSupBig, Delete 607020 rows due to missing values, raw data 840957 rows --> new data 233937 rows\n",
      "Var:EarningsConsistency, Delete 515111 rows due to missing values, raw data 840957 rows --> new data 325846 rows\n",
      "Var:EarningsForecastDisparity, Delete 284805 rows due to missing values, raw data 840957 rows --> new data 556152 rows\n",
      "Var:EarningsStreak, Delete 259441 rows due to missing values, raw data 840957 rows --> new data 581516 rows\n",
      "Var:EarningsSurprise, Delete 147779 rows due to missing values, raw data 840957 rows --> new data 693178 rows\n",
      "Var:EntMult, Delete 143914 rows due to missing values, raw data 840957 rows --> new data 697043 rows\n",
      "Var:EquityDuration, Delete 112258 rows due to missing values, raw data 840957 rows --> new data 728699 rows\n",
      "Var:ExclExp, Delete 102629 rows due to missing values, raw data 840957 rows --> new data 738328 rows\n",
      "Var:FEPS, Delete 51729 rows due to missing values, raw data 840957 rows --> new data 789228 rows\n",
      "Var:FR, Delete 517567 rows due to missing values, raw data 840957 rows --> new data 323390 rows\n",
      "Var:FirmAge, Delete 4125 rows due to missing values, raw data 840957 rows --> new data 836832 rows\n",
      "Var:ForecastDispersion, Delete 84871 rows due to missing values, raw data 840957 rows --> new data 756086 rows\n",
      "Var:Frontier, Delete 495573 rows due to missing values, raw data 840957 rows --> new data 345384 rows\n",
      "Var:GP, Delete 203596 rows due to missing values, raw data 840957 rows --> new data 637361 rows\n",
      "Var:GrAdExp, Delete 598582 rows due to missing values, raw data 840957 rows --> new data 242375 rows\n",
      "Var:GrLTNOA, Delete 119484 rows due to missing values, raw data 840957 rows --> new data 721473 rows\n",
      "Var:GrSaleToGrInv, Delete 255288 rows due to missing values, raw data 840957 rows --> new data 585669 rows\n",
      "Var:GrSaleToGrOverhead, Delete 255475 rows due to missing values, raw data 840957 rows --> new data 585482 rows\n",
      "Var:Herf, Delete 92946 rows due to missing values, raw data 840957 rows --> new data 748011 rows\n",
      "Var:HerfAsset, Delete 150450 rows due to missing values, raw data 840957 rows --> new data 690507 rows\n",
      "Var:HerfBE, Delete 150450 rows due to missing values, raw data 840957 rows --> new data 690507 rows\n",
      "Var:High52, Delete 7888 rows due to missing values, raw data 840957 rows --> new data 833069 rows\n",
      "Var:IdioVol3F, Delete 6793 rows due to missing values, raw data 840957 rows --> new data 834164 rows\n",
      "Var:IdioVolAHT, Delete 21967 rows due to missing values, raw data 840957 rows --> new data 818990 rows\n",
      "Var:Illiquidity, Delete 46244 rows due to missing values, raw data 840957 rows --> new data 794713 rows\n",
      "Var:IndMom, Delete 4153 rows due to missing values, raw data 840957 rows --> new data 836804 rows\n",
      "Var:IndRetBig, Delete 606982 rows due to missing values, raw data 840957 rows --> new data 233975 rows\n",
      "Var:IntMom, Delete 50017 rows due to missing values, raw data 840957 rows --> new data 790940 rows\n",
      "Var:IntanBM, Delete 276061 rows due to missing values, raw data 840957 rows --> new data 564896 rows\n",
      "Var:IntanCFP, Delete 256929 rows due to missing values, raw data 840957 rows --> new data 584028 rows\n",
      "Var:IntanEP, Delete 256929 rows due to missing values, raw data 840957 rows --> new data 584028 rows\n",
      "Var:IntanSP, Delete 259223 rows due to missing values, raw data 840957 rows --> new data 581734 rows\n",
      "Var:InvGrowth, Delete 406944 rows due to missing values, raw data 840957 rows --> new data 434013 rows\n",
      "Var:InvestPPEInv, Delete 188651 rows due to missing values, raw data 840957 rows --> new data 652306 rows\n",
      "Var:Investment, Delete 215606 rows due to missing values, raw data 840957 rows --> new data 625351 rows\n",
      "Var:LRreversal, Delete 131738 rows due to missing values, raw data 840957 rows --> new data 709219 rows\n",
      "Var:Leverage, Delete 67192 rows due to missing values, raw data 840957 rows --> new data 773765 rows\n",
      "Var:MRreversal, Delete 71775 rows due to missing values, raw data 840957 rows --> new data 769182 rows\n",
      "Var:MaxRet, Delete 4327 rows due to missing values, raw data 840957 rows --> new data 836630 rows\n",
      "Var:MeanRankRevGrowth, Delete 284644 rows due to missing values, raw data 840957 rows --> new data 556313 rows\n",
      "Var:Mom12m, Delete 46354 rows due to missing values, raw data 840957 rows --> new data 794603 rows\n",
      "Var:Mom12mOffSeason, Delete 27192 rows due to missing values, raw data 840957 rows --> new data 813765 rows\n",
      "Var:Mom6m, Delete 23402 rows due to missing values, raw data 840957 rows --> new data 817555 rows\n",
      "Var:Mom6mJunk, Delete 617899 rows due to missing values, raw data 840957 rows --> new data 223058 rows\n",
      "Var:MomOffSeason, Delete 88123 rows due to missing values, raw data 840957 rows --> new data 752834 rows\n",
      "Var:MomOffSeason06YrPlus, Delete 226991 rows due to missing values, raw data 840957 rows --> new data 613966 rows\n",
      "Var:MomOffSeason11YrPlus, Delete 347220 rows due to missing values, raw data 840957 rows --> new data 493737 rows\n",
      "Var:MomOffSeason16YrPlus, Delete 489921 rows due to missing values, raw data 840957 rows --> new data 351036 rows\n",
      "Var:MomSeason, Delete 88037 rows due to missing values, raw data 840957 rows --> new data 752920 rows\n",
      "Var:MomSeason06YrPlus, Delete 226493 rows due to missing values, raw data 840957 rows --> new data 614464 rows\n",
      "Var:MomSeason11YrPlus, Delete 347038 rows due to missing values, raw data 840957 rows --> new data 493919 rows\n",
      "Var:MomSeason16YrPlus, Delete 449039 rows due to missing values, raw data 840957 rows --> new data 391918 rows\n",
      "Var:MomSeasonShort, Delete 46204 rows due to missing values, raw data 840957 rows --> new data 794753 rows\n",
      "Var:NOA, Delete 112481 rows due to missing values, raw data 840957 rows --> new data 728476 rows\n",
      "Var:NetDebtFinance, Delete 192952 rows due to missing values, raw data 840957 rows --> new data 648005 rows\n",
      "Var:NetDebtPrice, Delete 569732 rows due to missing values, raw data 840957 rows --> new data 271225 rows\n",
      "Var:NetEquityFinance, Delete 160455 rows due to missing values, raw data 840957 rows --> new data 680502 rows\n",
      "Var:NetPayoutYield, Delete 304418 rows due to missing values, raw data 840957 rows --> new data 536539 rows\n",
      "Var:OPLeverage, Delete 67709 rows due to missing values, raw data 840957 rows --> new data 773248 rows\n",
      "Var:OperProf, Delete 309508 rows due to missing values, raw data 840957 rows --> new data 531449 rows\n",
      "Var:OperProfRD, Delete 292370 rows due to missing values, raw data 840957 rows --> new data 548587 rows\n",
      "Var:OrgCap, Delete 494377 rows due to missing values, raw data 840957 rows --> new data 346580 rows\n",
      "Var:PayoutYield, Delete 408481 rows due to missing values, raw data 840957 rows --> new data 432476 rows\n",
      "Var:PctAcc, Delete 115908 rows due to missing values, raw data 840957 rows --> new data 725049 rows\n",
      "Var:PredictedFE, Delete 504490 rows due to missing values, raw data 840957 rows --> new data 336467 rows\n",
      "Var:PriceDelayRsq, Delete 44678 rows due to missing values, raw data 840957 rows --> new data 796279 rows\n",
      "Var:PriceDelaySlope, Delete 44678 rows due to missing values, raw data 840957 rows --> new data 796279 rows\n",
      "Var:PriceDelayTstat, Delete 53911 rows due to missing values, raw data 840957 rows --> new data 787046 rows\n",
      "Var:RD, Delete 468912 rows due to missing values, raw data 840957 rows --> new data 372045 rows\n",
      "Var:RDS, Delete 162943 rows due to missing values, raw data 840957 rows --> new data 678014 rows\n",
      "Var:REV6, Delete 88021 rows due to missing values, raw data 840957 rows --> new data 752936 rows\n",
      "Var:RealizedVol, Delete 6464 rows due to missing values, raw data 840957 rows --> new data 834493 rows\n",
      "Var:ResidualMomentum, Delete 162901 rows due to missing values, raw data 840957 rows --> new data 678056 rows\n",
      "Var:ReturnSkew, Delete 6636 rows due to missing values, raw data 840957 rows --> new data 834321 rows\n",
      "Var:ReturnSkew3F, Delete 6794 rows due to missing values, raw data 840957 rows --> new data 834163 rows\n",
      "Var:RevenueSurprise, Delete 208244 rows due to missing values, raw data 840957 rows --> new data 632713 rows\n",
      "Var:RoE, Delete 65239 rows due to missing values, raw data 840957 rows --> new data 775718 rows\n",
      "Var:SP, Delete 67745 rows due to missing values, raw data 840957 rows --> new data 773212 rows\n",
      "Var:ShareIss1Y, Delete 71724 rows due to missing values, raw data 840957 rows --> new data 769233 rows\n",
      "Var:ShareIss5Y, Delete 213283 rows due to missing values, raw data 840957 rows --> new data 627674 rows\n",
      "Var:ShortInterest, Delete 255004 rows due to missing values, raw data 840957 rows --> new data 585953 rows\n",
      "Var:Tax, Delete 147796 rows due to missing values, raw data 840957 rows --> new data 693161 rows\n",
      "Var:TotalAccruals, Delete 149221 rows due to missing values, raw data 840957 rows --> new data 691736 rows\n",
      "Var:TrendFactor, Delete 17917 rows due to missing values, raw data 840957 rows --> new data 823040 rows\n",
      "Var:VarCF, Delete 141297 rows due to missing values, raw data 840957 rows --> new data 699660 rows\n",
      "Var:VolMkt, Delete 38447 rows due to missing values, raw data 840957 rows --> new data 802510 rows\n",
      "Var:VolSD, Delete 88173 rows due to missing values, raw data 840957 rows --> new data 752784 rows\n",
      "Var:VolumeTrend, Delete 113211 rows due to missing values, raw data 840957 rows --> new data 727746 rows\n",
      "Var:XFIN, Delete 159430 rows due to missing values, raw data 840957 rows --> new data 681527 rows\n",
      "Var:betaVIX, Delete 6833 rows due to missing values, raw data 840957 rows --> new data 834124 rows\n",
      "Var:cfp, Delete 115919 rows due to missing values, raw data 840957 rows --> new data 725038 rows\n",
      "Var:dNoa, Delete 108610 rows due to missing values, raw data 840957 rows --> new data 732347 rows\n",
      "Var:fgr5yrLag, Delete 326113 rows due to missing values, raw data 840957 rows --> new data 514844 rows\n",
      "Var:grcapx, Delete 165821 rows due to missing values, raw data 840957 rows --> new data 675136 rows\n",
      "Var:grcapx3y, Delete 201936 rows due to missing values, raw data 840957 rows --> new data 639021 rows\n",
      "Var:hire, Delete 66108 rows due to missing values, raw data 840957 rows --> new data 774849 rows\n",
      "Var:iomom_cust, Delete 252842 rows due to missing values, raw data 840957 rows --> new data 588115 rows\n",
      "Var:iomom_supp, Delete 256447 rows due to missing values, raw data 840957 rows --> new data 584510 rows\n",
      "Var:realestate, Delete 497573 rows due to missing values, raw data 840957 rows --> new data 343384 rows\n",
      "Var:retConglomerate, Delete 557399 rows due to missing values, raw data 840957 rows --> new data 283558 rows\n",
      "Var:roaq, Delete 85401 rows due to missing values, raw data 840957 rows --> new data 755556 rows\n",
      "Var:tang, Delete 537419 rows due to missing values, raw data 840957 rows --> new data 303538 rows\n",
      "Var:zerotrade12M, Delete 48992 rows due to missing values, raw data 840957 rows --> new data 791965 rows\n",
      "Var:zerotrade1M, Delete 7914 rows due to missing values, raw data 840957 rows --> new data 833043 rows\n",
      "Var:zerotrade6M, Delete 26727 rows due to missing values, raw data 840957 rows --> new data 814230 rows\n",
      "Var:PRC, Delete 4125 rows due to missing values, raw data 840957 rows --> new data 836832 rows\n",
      "Var:Size, Delete 4125 rows due to missing values, raw data 840957 rows --> new data 836832 rows\n",
      "Var:STreversal, Delete 7901 rows due to missing values, raw data 840957 rows --> new data 833056 rows\n"
     ]
    }
   ],
   "source": [
    "# Sharpe Ratio for these signals\n",
    "sharpe_res = []\n",
    "for signal in filter_res['Var'].values:\n",
    "    _,vwret = utils.SingleSort(df[['permno','YearMonth','ME','bh1m',signal]], 'permno', 'YearMonth', signal, 'bh1m', 5, 'ME', quantile_filter=None)\n",
    "    sharpe_res.append({'Var': signal, 'Sharpe': vwret['H-L'].mean()/vwret['H-L'].std()*np.sqrt(12)})\n",
    "    # break\n",
    "sharpe_res = pd.DataFrame(sharpe_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MKT: 0.4338233599897176\n"
     ]
    }
   ],
   "source": [
    "# MKT\n",
    "ff3 = pd.read_csv('../data/Other/FF3m_2023.csv')\n",
    "ff3 = ff3[ff3.yearMon<=201912]\n",
    "Mkt = ff3['Mkt.RF'].mean()/ff3['Mkt.RF'].std()*np.sqrt(12)\n",
    "print(f\"MKT: {Mkt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:Avg_bias, Delete 92854 rows due to missing values, raw data 840957 rows --> new data 748103 rows\n",
      "BHL: 0.9489550867146795\n"
     ]
    }
   ],
   "source": [
    "# BHL\n",
    "forecast_ORIG = pd.read_csv('../data/BHL/Conditional_Bias.csv')\n",
    "forecast_ORIG['YearMonth'] = pd.to_datetime(forecast_ORIG['DATE'],format='%Y-%m') + MonthEnd(0)\n",
    "df_ = df[['permno','YearMonth','ME','bh1m']].merge(forecast_ORIG,\n",
    "                                                   left_on=['permno','YearMonth'],\n",
    "                                                   right_on=['PERMNO','YearMonth'], how='left')\n",
    "_,vwret = utils.SingleSort(df_, 'permno', 'YearMonth', 'Avg_bias', 'bh1m', 5, 'ME', quantile_filter=None)\n",
    "BHL = -vwret['H-L'].mean()/vwret['H-L'].std()*np.sqrt(12)\n",
    "print(f\"BHL: {BHL}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:BE_Avg, Delete 84107 rows due to missing values, raw data 840957 rows --> new data 756850 rows\n",
      "RF: 0.14735437782644975\n"
     ]
    }
   ],
   "source": [
    "# RF without look-ahead bias\n",
    "df_tmp = pd.read_parquet('../data/Results/df_train_new.parquet')\n",
    "forecast_woLAB = pd.read_parquet('../data/Results/RF_wo_lookahead_raw_005.parquet')\n",
    "forecast_woLAB = forecast_woLAB.merge(df_tmp[['permno','YearMonth','prc_l1']], on=['permno','YearMonth'])\n",
    "\n",
    "forecast_woLAB['BE_Q1'] = (forecast_woLAB['AF_q1'] - forecast_woLAB['RF_q1']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_Q2'] = (forecast_woLAB['AF_q2'] - forecast_woLAB['RF_q2']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_Q3'] = (forecast_woLAB['AF_q3'] - forecast_woLAB['RF_q3']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_A1'] = (forecast_woLAB['AF_y1'] - forecast_woLAB['RF_y1']) / forecast_woLAB['prc_l1']\n",
    "forecast_woLAB['BE_A2'] = (forecast_woLAB['AF_y2'] - forecast_woLAB['RF_y2']) / forecast_woLAB['prc_l1']\n",
    "\n",
    "nonNA = (~forecast_woLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].isna()).sum(axis=1)\n",
    "forecast_woLAB['BE_Avg'] = np.where(nonNA>1,forecast_woLAB[['BE_Q1','BE_Q2','BE_Q3','BE_A1','BE_A2']].mean(axis=1,),np.nan)\n",
    "\n",
    "df_ = df[['permno','YearMonth','ME','bh1m']].merge(forecast_woLAB,\n",
    "                                                   on=['permno','YearMonth'],\n",
    "                                                   how='left')\n",
    "_,vwret = utils.SingleSort(df_, 'permno', 'YearMonth', 'BE_Avg', 'bh1m', 5, 'ME', quantile_filter=None)\n",
    "RF = -vwret['H-L'].mean()/vwret['H-L'].std()*np.sqrt(12)\n",
    "print(f\"RF: {RF}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 381,
       "width": 881
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['Times New Roman']\n",
    "fig, ax = plt.subplots(1,1, figsize=(9,4))\n",
    "sharpe_res['Sharpe'].hist(bins=np.arange(-0.35,0.81,0.05), ax=ax, color='lightgrey', edgecolor='black')\n",
    "ax.tick_params(axis='x', which='both', direction='in', top=True, length=5, labelfontfamily='Cambria')\n",
    "ax.tick_params(axis='y', which='both', direction='in', right=True, length=5, labelfontfamily='Cambria')\n",
    "ax.set_xticks(np.arange(-0.4,1.1,0.1), )\n",
    "ax.set_yticks(np.arange(0,20,5))\n",
    "ax.grid(False, axis='both')\n",
    "ax.set_xlabel('Annualized Sharpe Ratio')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.margins(x=0.01, y=0.05)\n",
    "y_min, y_max = ax.get_ylim()\n",
    "ax.set_ylim(y_min - 0.5, y_max)\n",
    "ax.set_xlim(-0.41, 1.01)\n",
    "\n",
    "#\n",
    "arrows = [\n",
    "    {\"x\": 0.43, \"y\": 15, \"text\": \"Market = 0.43\", \"arrow_y\": 14},\n",
    "    {\"x\": 0.95, \"y\": 18, \"text\": \"BHL with look-ahead bias = 0.95\", \"arrow_y\": 17},\n",
    "    {\"x\": 0.15, \"y\": 12, \"text\": \"BHL without look-ahead bias = 0.09\", \"arrow_y\": 11},\n",
    "]\n",
    "\n",
    "# Mkt\n",
    "ax.axvline(Mkt, color='blue', linestyle='-')\n",
    "ax.annotate(\n",
    "        \"Market = 0.43\", xy=(Mkt, 13),\n",
    "        xytext=(Mkt - 0.1, 14.5),\n",
    "        arrowprops=dict(\n",
    "            arrowstyle=\"-|>\", color=\"black\", lw=1.5\n",
    "        ),   fontsize=10, ha=\"center\",\n",
    "    )\n",
    "\n",
    "\n",
    "# BHL\n",
    "ax.axvline(BHL, color='black', linestyle='-')\n",
    "ax.text(BHL-0.22, 12, \"BHL with\\nlook-ahead bias\", fontsize=10, ha='center')\n",
    "ax.annotate(\n",
    "        \"= 0.95\",  xy=(BHL, 11),\n",
    "        xytext=(BHL - 0.105, 12.5),\n",
    "        arrowprops=dict(\n",
    "            arrowstyle=\"-|>\", color=\"black\", lw=1.5\n",
    "        ),\n",
    "        fontsize=10, ha=\"center\",\n",
    "    )\n",
    "\n",
    "# RF without look-ahead bias\n",
    "ax.axvline(RF, color='red', linestyle='-')\n",
    "ax.text(RF-0.32, 15, \"BHL without\\nlook-ahead bias\", fontsize=10, ha='center')\n",
    "ax.annotate(\n",
    "        \"= 0.15\",  xy=(RF, 14),\n",
    "        xytext=(RF - 0.205, 15.5),\n",
    "        arrowprops=dict(\n",
    "            arrowstyle=\"-|>\", color=\"black\", lw=1.5\n",
    "        ),\n",
    "        fontsize=10, ha=\"center\",\n",
    "    )\n",
    "plt.tight_layout()\n",
    "plt.savefig('../data/Results/ASR_3.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Var</th>\n",
       "      <th>Sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>MomSeason</td>\n",
       "      <td>0.622847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Tax</td>\n",
       "      <td>0.643878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>IndRetBig</td>\n",
       "      <td>0.658423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CBOperProf</td>\n",
       "      <td>0.665432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>MomSeason06YrPlus</td>\n",
       "      <td>0.778765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Var    Sharpe\n",
       "91           MomSeason  0.622847\n",
       "124                Tax  0.643878\n",
       "69           IndRetBig  0.658423\n",
       "18          CBOperProf  0.665432\n",
       "92   MomSeason06YrPlus  0.778765"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe_res.sort_values(by='Sharpe').tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var:fgr5yrLag, Delete 171530 rows due to missing values, raw data 466002 rows --> new data 294472 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>H-L</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ret</th>\n",
       "      <td>0.670</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(1.29)</td>\n",
       "      <td>(1.52)</td>\n",
       "      <td>(2.36)</td>\n",
       "      <td>(2.15)</td>\n",
       "      <td>(3.79)</td>\n",
       "      <td>(0.34)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1       2       3       4       5     H-L\n",
       "Ret   0.670   0.534   0.730   0.645   0.819   0.149\n",
       "     (1.29)  (1.52)  (2.36)  (2.15)  (3.79)  (0.34)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## [La Porta (1996) average return in footnote 1]\n",
    "# La Port 1996 in post_2000 sample\n",
    "crsp_2000 = crsp[crsp['YearMonth'] >= '2000-01-01']\n",
    "df_ = crsp_2000[['permno','YearMonth','ME','bh1m']].merge(osap[['permno','YearMonth','fgr5yrLag']], on=['permno','YearMonth'], how='left')\n",
    "_,vwret = utils.SingleSort(df_,\n",
    "                           'permno', 'YearMonth',\n",
    "                           'fgr5yrLag', 'bh1m', 5, 'ME',\n",
    "                           quantile_filter=None)\n",
    "utils.SingleSort_RetAna(_, vwret, 'YearMonth', factor_data=None, factor_dict={'Ret':['ones']}, lag=12, float_format='%.3f')"
   ]
  }
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